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Stride 2 1-D, 2-D, and 3-D Winograd for Convolutional Neural Networks

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... Paper [34] proposes the decomposable Winograd method (DWM), which expands the usage range of Winograd to other types of kernels. Paper [30] proposes a similar method for the stride of 2 kernel, and validates two types of kernels on FPGA. In addition, paper [30] considers how to save Look-Up Table (LUT) resources when supporting two types of kernels. ...
... Paper [30] proposes a similar method for the stride of 2 kernel, and validates two types of kernels on FPGA. In addition, paper [30] considers how to save Look-Up Table (LUT) resources when supporting two types of kernels. Paper [35] proposes a more efficient approach for the kernel size of 3  3 and stride of 2 and eventually saves 49.7% of LUT resources compared with paper [30]. ...
... In addition, paper [30] considers how to save Look-Up Table (LUT) resources when supporting two types of kernels. Paper [35] proposes a more efficient approach for the kernel size of 3  3 and stride of 2 and eventually saves 49.7% of LUT resources compared with paper [30]. But papers [30,35] neglect considering how to save LUT resources when types of kernels are larger than 2. ...
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The programmability of FPGA suits the constantly changing convolutional neural network (CNN). However, several challenges arise when the previous FPGA-based accelerators update CNN. Firstly, although the model of RepVGG can balance accuracy and speed, it solely supports two types of kernels. Meanwhile, 8-bit integer-only quantization of PyTorch which can support various CNNs is seldom successfully supported by the FPGA-based accelerators. In addition, Winograd F(4 × 4, 3 × 3) uses less multiplication, but its transformation matrix contains irregular decimals, which could lead to accuracy problems. To tackle these issues, this paper proposes High-accuracy Branch-fused CNN Accelerator (HBCA): a toolchain and corresponding FPGA-based accelerator. The toolchain proposes inception-based branch–fused technique, which can support more types of kernels. Meanwhile, the accelerator proposes Winograd-quantization dual decimal–fuse techniques to balance accuracy and speed. In addition, this accelerator supports multi-types of kernels and proposes Winograd decomposed-part reuse, multi-mode BRAM & DSP and data reuse to increase power efficiency. Experiments show that HBCA is capable of supporting seven CNNs with different types of kernels and more branches. The accuracy loss is within 0.1% when compared to the quantized model. Furthermore, the power efficiency (GOPS/W) of Inception, ResNet and VGG is up to 226.6, 188.1 and 197.7, which are better than other FPGA-based CNN accelerators.
... CNN is the most extensively used image classification method, and it's employed in a lot of applications. CNN's require a lot of computing and memory [3]. Convolution layers (CONV) and FC layers are two of them that are memory-bound and compute-bound, respectively [9]. ...
... In contrast to the traditional CNN, the Winograd algorithm is used in this study. It is derived based on Chinese Remainder Theorem [3]. Since using traditional CNN is more expensive and takes several multiplications. ...
... The accuracies reported by the mentioned boards were 86.66, 84.66, and 91.4. With efficiencies of 0.18, 1.32, and 1.33 (GOPs/DSP), the performances were 137, 3044, and 1788 (GOPs) respectively [3]. ...
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The convolutional neural network (CNN) is the most widely used machine learning technique within the fields of image and video processing. It is primarily used to categorize images using vast datasets. This require a lot of calculations. The effectiveness of a field-programmable gate array (FPGA) as a hardware accelerator for CNNs will give excellent performance at low power budgets. The employment of the Winograd algorithm can reduce the number of processing stages in CNN. 2-D convolution is employed for the bulk of calculations in CNNs. The tactic for computing convolution for smaller filter sizes that uses Winograd minimum filtering is the handiest. The comparison of computation complexity for multiplications will be performed using Matlab. The architecture of the Winograd-based processing element, RTL coding in Verilog HDL, and the test bench are designed to examine the performance. The Xilinx Vivado / Cadence tool will be used to implement the processing element in the convolution unit on an FPGA or ASIC.
... Various convolution accelerators based on the Winograd algorithm have been proposed [28][29][30][31]. Moreover, [32,33] proposed a method of optimizing 2-stride convolution with the Winograd algorithm, which reduces the design complexity of the Winograd-based CNN accelerator and significantly enhances the computation efficiency. However, the Winograd algorithm is not the best optimization scheme for the upsampling in U-Net because of the sparse feature map after expansion. ...
... Such large transformation matrices result in complex pre-computation and more latency. Yang et al. [32] and Yepez and Ko [33] further applied the Winograd algorithm for 2-stride convolution by decomposing the input feature map titles and kernels. ...
... Refs. [32,33] proposed a strategy of decomposing 2-D 2-stride convolution, that is, decomposing and recombining the input feature title and convolution kernel according to the location of elements. Then, each decomposed feature sub-titles only do convolution with the corresponding decomposed sub-kernel. ...
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Real-time object detection is a challenging but crucial task for autonomous underwater vehicles because of the complex underwater imaging environment. Resulted by suspended particles scattering and wavelength-dependent light attenuation, underwater images are always hazy and color-distorted. To overcome the difficulties caused by these problems to underwater object detection, an end-to-end CNN network combined U-Net and MobileNetV3-SSDLite is proposed. Furthermore, the FPGA implementation of various convolution in the proposed network is optimized based on the Winograd algorithm. An efficient upsampling engine is presented, and the FPGA implementation of squeeze-and-excitation module in MobileNetV3 is optimized. The accelerator is implemented on a Zynq XC7Z045 device running at 150 MHz and achieves 23.68 frames per second (fps) and 33.14 fps when using MobileNetV3-Large and MobileNetV3-Small as the feature extractor. Compared to CPU, our accelerator achieves 7.5×–8.7× speedup and 52×–60× energy efficiency.
... The authors of [8] presented a software implementation of Winograd method and applied it in a convolutional layer of a neural network with calculations on a graphical processor. In [9], the authors developed a hardware accelerator on Field-Programmable Gate Array (FPGA) based on the Winograd method for the convolutional layer of the neural network. ...
... A comparison is made of the proposed filter architecture with computations in RNS and the known filter architecture with computations in PNS [9]. The parameters of filters with a finite impulse response based on multiplyaccumulate (MAC) blocks were also calculated, we denote them as FIR MAC , then for 2 × 2 filter mask the delay and area parameters are calculated as follows [23]: ...
... Theoretical analysis based on the ''unit-gate'' model of the proposed device parameters showed that RNS usage allows to reduce the device delay by 24.79% -66.77%, and the area device by 17.59% -53.67%, compared with the known implementation based on Winograd filtering in PNS [9]. In addition, the proposed device architecture has 13.47% -42.04% less delay, and 2.20% -18.03% less area, except for the 8-bit device, which has a 47.38% larger area than the known MAC-based filter architecture [23]. ...
Article
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Improving the technical characteristics of digital signal processing devices is an important problem in many practical tasks. According to the Winograd method, the paper proposes the architecture of a device for two-dimensional filtering in a residue number system (RNS) with moduli of a special type. The work carried out the technical parameters theoretical analysis of the proposed filter architecture for different RNS moduli sets by the "unit-gate"-model. In addition, the proposed architecture is compared with known digital filter implementations. The theoretical analysis results showed that the proposed filter architecture makes it possible to increase the signal processing speed by 1.33 – 6.90 times, compared with the known device implementations. Also, in the paper, the hardware simulation of the proposed filter architecture was performed on FPGA, which showed that the performance of the proposed device is 1.31 – 4.12 times higher than known digital filter architectures. The research results can be used in digital signal processing systems to increase their performance and reduce hardware costs. In addition, the developed architectures can be applied in the development of hardware accelerators for complex digital signals analysis systems.
... These results highlight the efficacy and superiority of 3D CNNs over conventional methods. [11,17,18], Single-FPGA [1,3,5,19,20], Multi-FPGA [1], CPU [21,22], Xeon Phi [12,21], GPU [22][23][24], CPU-GPU [22], DSP [14], resistive RAM [25] CONV style Direct CONV [3,4,11,13,[21][22][23]26], matrix multiplication based [20], FFT-based [13,22], Winograd-based [1,12,14,18,19,23] 3D CNN evaluated C3D [3, 4, 11, 12, 17, 19-21, 27, 28], Base3D [28], I3D [11,17], 3D ResNet-50 [11,17], 3D U-Net [12], S3D [27], V3D [23], E3DNet [5], R(2+1)D [27,29] Dataset 3D MNIST [25], UCF101 [3,5,17,18,27], Sports-1M [4], LUNA-16 [1], Kinetics [11,17], TRECVID [18] Use of framework/library Intel Thread Building Block [22], MKL [5,12,13,22,26], FFTW [13,22], cuDNN [12,22], cuFFT [22], cuBLAS [23] Comparison with Caffe [13,24], Theano [13], Pytorch [26], TensorFlow [26], cuDNN [23,24,26] , LIBXSMM [12], MNN [27], MXNet [5] 3 COMPUTING PLATFORMS FOR 3D CNNS Table 1 gives the classification of various research works based on key parameters. It shows their computing platforms, the strategy for realizing CONV and the 3D CNN workload used by them. ...
... These results highlight the efficacy and superiority of 3D CNNs over conventional methods. [11,17,18], Single-FPGA [1,3,5,19,20], Multi-FPGA [1], CPU [21,22], Xeon Phi [12,21], GPU [22][23][24], CPU-GPU [22], DSP [14], resistive RAM [25] CONV style Direct CONV [3,4,11,13,[21][22][23]26], matrix multiplication based [20], FFT-based [13,22], Winograd-based [1,12,14,18,19,23] 3D CNN evaluated C3D [3, 4, 11, 12, 17, 19-21, 27, 28], Base3D [28], I3D [11,17], 3D ResNet-50 [11,17], 3D U-Net [12], S3D [27], V3D [23], E3DNet [5], R(2+1)D [27,29] Dataset 3D MNIST [25], UCF101 [3,5,17,18,27], Sports-1M [4], LUNA-16 [1], Kinetics [11,17], TRECVID [18] Use of framework/library Intel Thread Building Block [22], MKL [5,12,13,22,26], FFTW [13,22], cuDNN [12,22], cuFFT [22], cuBLAS [23] Comparison with Caffe [13,24], Theano [13], Pytorch [26], TensorFlow [26], cuDNN [23,24,26] , LIBXSMM [12], MNN [27], MXNet [5] 3 COMPUTING PLATFORMS FOR 3D CNNS Table 1 gives the classification of various research works based on key parameters. It shows their computing platforms, the strategy for realizing CONV and the 3D CNN workload used by them. ...
... These results highlight the efficacy and superiority of 3D CNNs over conventional methods. [11,17,18], Single-FPGA [1,3,5,19,20], Multi-FPGA [1], CPU [21,22], Xeon Phi [12,21], GPU [22][23][24], CPU-GPU [22], DSP [14], resistive RAM [25] CONV style Direct CONV [3,4,11,13,[21][22][23]26], matrix multiplication based [20], FFT-based [13,22], Winograd-based [1,12,14,18,19,23] 3D CNN evaluated C3D [3, 4, 11, 12, 17, 19-21, 27, 28], Base3D [28], I3D [11,17], 3D ResNet-50 [11,17], 3D U-Net [12], S3D [27], V3D [23], E3DNet [5], R(2+1)D [27,29] Dataset 3D MNIST [25], UCF101 [3,5,17,18,27], Sports-1M [4], LUNA-16 [1], Kinetics [11,17], TRECVID [18] Use of framework/library Intel Thread Building Block [22], MKL [5,12,13,22,26], FFTW [13,22], cuDNN [12,22], cuFFT [22], cuBLAS [23] Comparison with Caffe [13,24], Theano [13], Pytorch [26], TensorFlow [26], cuDNN [23,24,26] , LIBXSMM [12], MNN [27], MXNet [5] 3 COMPUTING PLATFORMS FOR 3D CNNS Table 1 gives the classification of various research works based on key parameters. It shows their computing platforms, the strategy for realizing CONV and the 3D CNN workload used by them. ...
Article
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3D convolution neural networks (CNNs) have shown excellent predictive performance on tasks such as action recognition from videos. Since 3D CNNs have unique characteristics and extremely high compute/memory-overheads, executing them on accelerators designed for 2D CNNs provides sub-optimal performance. To overcome these challenges, researchers have recently proposed architectures for 3D CNNs. In this paper, we present a survey of hardware accelerators and hardware-aware algorithmic optimizations for 3D CNNs. We include only those CNNs that perform 3D convolution and not those that perform only 2D convolution on 2D or 3D data. We highlight their key ideas and underscore their similarities and differences. We believe that this survey will spark a great deal of research towards the design of ultra-efficient 3D CNN accelerators of tomorrow.
... As not all convolutional layers can be implemented efficiently with the Winograd algorithm, we only replace those with a 3×3 kernel size and unitary stride, whereas all the others, like the 1×1 pointwise convolutions, are processed using a standard algorithm. Although strided convolution can be implemented with the Winograd algorithm [65], [67], the control and compute overhead dominates the potential MACs reduction (i.e., stride-2 F 4 leads only to a 1.8× MACs reduction). ...
... VI. RELATED WORK Winograd Algorithm. Several works have been proposed to extend the original Winograd algorithm [62] to work on general 2D convolution [29], [65], [67], and to improve its performance by combining it with the Strassen algorithm [69] or its numerical accuracy by using higher-order polynomials [4] and better polynomial root points for m > 4 [1], [3]. Li et al. [34] combined the Winograd algorithm with AdderNet, which uses 1 instead of 2 norm for feature extraction, therefore replacing all MAC operations with additions. ...
Preprint
Most of today's computer vision pipelines are built around deep neural networks, where convolution operations require most of the generally high compute effort. The Winograd convolution algorithm computes convolutions with fewer MACs compared to the standard algorithm, reducing the operation count by a factor of 2.25x for 3x3 convolutions when using the version with 2x2-sized tiles $F_2$. Even though the gain is significant, the Winograd algorithm with larger tile sizes, i.e., $F_4$, offers even more potential in improving throughput and energy efficiency, as it reduces the required MACs by 4x. Unfortunately, the Winograd algorithm with larger tile sizes introduces numerical issues that prevent its use on integer domain-specific accelerators and higher computational overhead to transform input and output data between spatial and Winograd domains. To unlock the full potential of Winograd $F_4$, we propose a novel tap-wise quantization method that overcomes the numerical issues of using larger tiles, enabling integer-only inference. Moreover, we present custom hardware units that process the Winograd transformations in a power- and area-efficient way, and we show how to integrate such custom modules in an industrial-grade, programmable DSA. An extensive experimental evaluation on a large set of state-of-the-art computer vision benchmarks reveals that the tap-wise quantization algorithm makes the quantized Winograd $F_4$ network almost as accurate as the FP32 baseline. The Winograd-enhanced DSA achieves up to 1.85x gain in energy efficiency and up to 1.83x end-to-end speed-up for state-of-the-art segmentation and detection networks.
... For higher accuracy, a stride method, a technique frequently used in the convolutional neural network (CNN) [24], was introduced to our learning group. Due to the nature of learning through the number of times, a pattern appears at a specific location, a pattern that has a similar shape but has a different location within the image may be recognized as a completely different pattern. ...
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In this paper, we present a digital processing in memory (DPIM) configured as a stride edge-detection search frequency neural network (SE-SFNN) which is trained through a spike-location-dependent-plasticity (SLDP), a learning mechanism reminiscent of spike-timing-dependent plasticity. This mechanism allows for rapid online learning as well as a simple memory-based implementation. In particular, we employ a ternary data scheme to take advantage of a ternary content addressable memory (TCAM). The scheme utilizes a ternary representation of the image pixels and the TCAMs are used in a two-layer format to significantly reduce the computation time. The first layer applies several filtering kernels followed by the second layer that reorders pattern dictionaries of TCAMs to place the most frequent patterns at the top of each supervised TCAM dictionary. Numerous TCAM blocks in both layers operate in a massively parallel fashion using digital ternary values. There are no complicated multiply operations performed and learning is performed in a feedforward scheme. This allows rapid robust learning as a trade-off with the parallel memory block size. Furthermore, we propose a method to reduce the TCAM memory size using a two-tiered minor to major promotion (M2MP) of frequently occurring patterns. This reduction scheme is performed concurrently during the learning operation without incurring a preconditioning overhead. We show that with a minimal circuit overhead, the required memory size is reduced by 84.4% and the total clock cycles required for learning also decrease by 97.31 % while the accuracy decreases only by 1.12%. We classified images with 94.58% accuracy on the MNIST dataset. Using a 100MHz clock, our simulation results show that the MNIST training takes about 6.3 ms dissipating less than 4mW of average power. In terms of the inference speed, the trained hardware is capable of processing 5,882,352 images per second.
... Stride is a parameter or constraint that can determine the amount of filter shift. If the value of a stride is 1, then the convolutional filter will shift by 1 pixel moving horizontally and vertically (Yepez & Ko, 2020). The smaller the value of a stride, the more detailed the value of the information we will get on input, but this stride process has a large computation time (Awangga & Putro, 2020). ...
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In the coastal area of Likupang, many types of saltwater fish can be consumed, such as tuna and skipjack. Yet, there are also types of saltwater fish that cannot be consumed or protected by the government, such as Napoleon fish and sea kingfish. Thus, this research aimed to build a desktop application that can automatically classify consumable and non-consumable saltwater fish species more accurately and promptly using a suitable image recognition method like the Convolutional Neural Network (CNN). CNN has abilities to distinguish images by recognizing several pixels in a two-dimensional image and RGB (Red, Green, Blue) colors which are then converted into a matrix with various values, making it easier for the system to recognize the two-dimensional image. By using 40% test data (143 images) and 60% training data (213 images), this study obtained test accuracy in identifying and classifying images of consumable fish, non-consumable fish, and non-fish images with each percentage of 94%, 98%, and 95% respectively. Abstrak-Di perairan Likupang terdapat banyak jenis ikan air asin yang bisa dikonsumsi, seperti ikan Tuna dan Cakalang. Namun, ada juga jenis ikan air asin yang tidak bisa dikonsumsi atau dilindungi oleh pemerintah, seperti ikan Napoleon dan ikan Raja Laut. Oleh karena itu, penelitian ini bertujuan untuk membangun aplikasi desktop yang dapat secara otomatis mengklasifikasikan spesies ikan laut yang dapat dikonsumsi dan tidak dapat dikonsumsi dengan lebih akurat dan cepat menggunakan metode pengenalan citra yang sesuai seperti Convolutional Neural Network (CNN). CNN memiliki kemampuan untuk membedakan gambar dengan mengenali beberapa piksel pada gambar dua dimensi dan warna RGB (Red, Green, Blue) yang kemudian diubah menjadi matriks dengan berbagai nilai, sehingga memudahkan sistem untuk mengenali gambar dua dimensi tersebut. Dengan menggunakan 40% data uji (143 citra) dan 60% data latih (213 citra), penelitian ini mendapatkan akurasi uji dalam mengidentifikasi dan mengklasifikasikan citra ikan konsumsi, ikan tidak konsumsi, dan citra non-ikan dengan persentase masing-masing 94% , 98%, dan 95%.
... Therefore, we adapt two 8x8 multiplications into one DSP48E to reduce the total DSP usage ( Figure 5). For the DWC Accelerator shown in Figure 6, many mature and efficient methods for a standard 3 * 3 convolution have been proposed, such as Winograd [26,27]. Because the DWC part needs to match the PWC output and one kernel is only used in one channel, we keep the data unchanged and feed it into a line buffer unit directly to complete the DWC. ...
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Convolutional neural networks (CNNs) have been widely applied in the fields of medical tasks because they can achieve high accuracy in many fields using a large number of parameters and operations. However, many applications designed for auxiliary checks or help need to be deployed into portable devices, where the huge number of operations and parameters of a standard CNN can become an obstruction. MobileNet adopts a depthwise separable convolution to replace the standard convolution, which can greatly reduce the number of operations and parameters while maintaining a relatively high accuracy. Such highly structured models are very suitable for FPGA implementation in order to further reduce resource requirements and improve efficiency. Many other implementations focus on performance more than on resource requirements because MobileNets has already reduced both parameters and operations and obtained significant results. However, because many small devices only have limited resources they cannot run MobileNet-like efficient networks in a normal way, and there are still many auxiliary medical applications that require a high-performance network running in real-time to meet the requirements. Hence, we need to figure out a specific accelerator structure to further reduce the memory and other resource requirements while running MobileNet-like efficient networks. In this paper, a MobileNet accelerator is proposed to minimize the on-chip memory capacity and the amount of data that is transferred between on-chip and off-chip memory. We propose two configurable computing modules: Pointwise Convolution Accelerator and Depthwise Convolution Accelerator, to parallelize the network and reduce the memory requirement with a specific dataflow model. At the same time, a new cache usage method is also proposed to further reduce the use of the on-chip memory. We implemented the accelerator on Xilinx XC7Z020, deployed MobileNetV2 on it, and achieved 70.94 FPS with 524.25 KB on-chip memory usage under 150 MHz.
... В статье [9] предложенные алгоритмы цифровой фильтрации на основе метода Винограда для сверточных слоев нейронных сетей показали превосходство над быстрым преобразованием Фурье по скорости работы глубокой нейронной сети при обработке больших массивов визуальных данных. Данный подход был расширен и обобщен на случаи обработки одномерных, двумерных и трехмерных сигналов сверточной нейронной сетью [10]. На базе данных исследований разработаны различные архитектуры [11] и аппаратные ускорители [12 -14] для высокопроизводительной реализации алгоритмов нейросетевой обработки изображений на основе метода Винограда. ...
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The fast increase of the amount of quantitative and qualitative characteristics of digital visual data calls for the improvement of the performance of modern image processing devices. This article proposes new algorithms for 2D digital image processing based on the Winograd method in a general form. An analysis of the obtained results showed that the use of the Winograd method reduces the computational complexity of image processing by up to 84% compared to the traditional direct digital filtering method depending on the filter parameters and image fragments, while not affecting the quality of image processing. The resulting Winograd method transformation matrices and the algorithms developed can be used in image processing systems to improve the performance of the modern microelectronic devices that carry out image denoising, compression, and pattern recognition. Research directions that show promise for further research include hardware implementation on a field-programmable gate array and application-specific integrated circuit, development of algorithms for digital image processing based on the Winograd method in a general form for a 1D wavelet filter bank and for stride convolution used in convolutional neural networks.
... Besides, we compared our proposed model on COVID-19 diagnosis tasks with those of previous SOTA COVID- 19 screening methods, which included Shi [47], Wang [48], and Xu [47] in Table 3. Shi [47] presented an infection region-specific segmentation technique based on a random forest model to distinguish COVID-19 from other forms of pneumonia using CT exams [47]. This study reported 83.30% accuracy [47]. ...
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In this paper, a two-dimensional Winograd CNN (Convolutional Neural Network) chip for COVID-19 and pneumonia detection is proposed. In light of the COVID-19 pandemic, many studies have led to a dramatic increase in the effects of the virus on the lungs. Some studies have also pointed out that the clinical application of deep learning in the medical field is also increasing, and it is also pointed out that the radiation impact of CT exposure is more serious than that of X-ray films and that CT exposure is not suitable for viral pneumonia. This study will analyze the results of X-rays trained using CNN architecture and convolutional using Winograd. This research will also set up a popular model architecture to realize four kinds of grayscale image prediction to verify the actual prediction effect on this data. The experimental data is mainly composed of chest X-rays of four different types of grayscales as input material. Among them, the research method of this experiment is to design the basic CNN operation structure of the chip and apply the Winograd calculus method to the convolutional operation. Finally, according to the TSMC 0.18 μm process, the actual chip is produced, and each step is verified to ensure the correctness of the circuit. The experimental results prove that the accuracy of our proposed method reaches 87.87%, and the precision reaches 88.48%. This proves that our proposed method has an excellent recognition rate.
... Moreover, the nature of the Winograd transformation is only applicable to convolutions with stride s = 1. Making the Winograd transformation work for stride s > 1 is an open research problem with multiple solutions having been proposed (Pan and Chen 2021;Huang et al. 2021;Yepez and Ko 2020) in the recent past. Using Winograd convolution for r = 7 causes a lack of numerical precision, and hence we avoid it. ...
Preprint
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ML-as-a-service continues to grow, and so does the need for very strong privacy guarantees. Secure inference has emerged as a potential solution, wherein cryptographic primitives allow inference without revealing users' inputs to a model provider or model's weights to a user. For instance, the model provider could be a diagnostics company that has trained a state-of-the-art DenseNet-121 model for interpreting a chest X-ray and the user could be a patient at a hospital. While secure inference is in principle feasible for this setting, there are no existing techniques that make it practical at scale. The CrypTFlow2 framework provides a potential solution with its ability to automatically and correctly translate clear-text inference to secure inference for arbitrary models. However, the resultant secure inference from CrypTFlow2 is impractically expensive: Almost 3TB of communication is required to interpret a single X-ray on DenseNet-121. In this paper, we address this outstanding challenge of inefficiency of secure inference with three contributions. First, we show that the primary bottlenecks in secure inference are large linear layers which can be optimized with the choice of network backbone and the use of operators developed for efficient clear-text inference. This finding and emphasis deviates from many recent works which focus on optimizing non-linear activation layers when performing secure inference of smaller networks. Second, based on analysis of a bottle-necked convolution layer, we design a X-operator which is a more efficient drop-in replacement. Third, we show that the fast Winograd convolution algorithm further improves efficiency of secure inference. In combination, these three optimizations prove to be highly effective for the problem of X-ray interpretation trained on the CheXpert dataset.
... WHD [16] exploited the fusion of Winograd unit, but only one type of unit can be used in per-layer. Reference [22] proposed a Winograd processing element for convolutions which only compatible with filter size 3 and both stride 1 and 2. Reference [17,19] introduced the universal approach to deal with the large stride and large filter size. Previous design space exploration schemes have been applicable only to 2D CNN accelerators, making them unsuitable for different dimension architecture. ...
Article
Convolutional neural networks (CNNs) have proven to be promising in various applications such as audio recognition, image classification, and video understanding. Winograd algorithm helps to reduce the complexity of computation in a convolution but suffers from poor compatibility for different convolution shapes. This work introduces a dynamic dimension-level fusion architecture based on Winograd for accelerating different dimensions of CNNs. We explore this Winograd architecture by designing Dimension Fusion, a dimension-level processing engine that dynamically fuses to match the convolution shape of individual CNN layers. The proposed architecture is the first work based on Winograd algorithm to be compatible with all convolution shapes (dimension, stride, and filter-size) and achieves highest PE efficiency up to 1.55x and energy efficiency up to 3.3x compared with the state-of-art accelerators.
... Down-sampling often uses a stride convolution operator to reduce the size of the feature map through non-unit step convolution. [19] extended the algorithm to three dimensions while achieving a step size of 2. ...
Preprint
Convolutional Neural Network (CNN) has been widely used in various fields and played an important role. Convolution operators are the fundamental component of convolutional neural networks, and it is also the most time-consuming part of network training and inference. In recent years, researchers have proposed several fast convolution algorithms including FFT and Winograd. Among them, Winograd convolution significantly reduces the multiplication operations in convolution, and it also takes up less memory space than FFT convolution. Therefore, Winograd convolution has quickly become the first choice for fast convolution implementation within a few years. At present, there is no systematic summary of the convolution algorithm. This article aims to fill this gap and provide detailed references for follow-up researchers. This article summarizes the development of Winograd convolution from the three aspects of algorithm expansion, algorithm optimization, implementation, and application, and finally makes a simple outlook on the possible future directions.
... Down-sampling often uses a stride convolution operator to reduce the size of the feature map through non-unit step convolution. [19] extended the algorithm to three dimensions while achieving a step size of 2. ...
Conference Paper
Convolutional Neural Network (CNN) has been widely used in various fields and played an important role. Convolution operators are the fundamental component of convolutional neural networks, and it is also the most time-consuming part of network training and inference. In recent years, researchers have proposed several fast convolution algorithms including FFT and Winograd. Among them, Winograd convolution significantly reduces the multiplication operations in convolution, and it also takes up less memory space than FFT convolution. Therefore, Winograd convolution has quickly become the first choice for fast convolution implementation within a few years. At present, there is no systematic summary of the convolution algorithm. This article aims to fill this gap and provide detailed references for follow-up researchers. This article summarizes the development of Winograd convolution from the three aspects of algorithm expansion, algorithm optimization, implementation, and application, and finally makes a simple outlook on the possible future directions.
... The first layer of our customized backbone contains a CNN with stride 2; this downsamples the input without a max pooling layer and requires less computation than stride one [50]. ...
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... Two types of strides are used: Stride 1 (s1) and Stride 2 (s2). Stride represents the element-wise shift displacement of a kernel over an input along a particular axis [11]. Stride 1 will move one filter at a time, and Stride 2 will move two filters at a time. ...
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Convolutional neural nets (CNNs) have become a practical means to perform vision tasks, particularly in the area of image classification. FPGAs are well known to be able to perform convolutions efficiently, however, most recent efforts to run CNNs on FPGAs have shown limited advantages over other devices such as GPUs. Previous approaches on FPGAs have often been memory bound due to the limited external memory bandwidth on the FPGA device. We show a novel architecture written in OpenCL(TM), which we refer to as a Deep Learning Accelerator (DLA), that maximizes data reuse and minimizes external memory bandwidth. Furthermore, we show how we can use the Winograd transform to significantly boost the performance of the FPGA. As a result, when running our DLA on Intel's Arria 10 device we can achieve a performance of 1020 img/s, or 23 img/s/W when running the AlexNet CNN benchmark. This comes to 1382 GFLOPs and is 10x faster with 8.4x more GFLOPS and 5.8x better efficiency than the state-of-the-art on FPGAs. Additionally, 23 img/s/W is competitive against the best publicly known implementation of AlexNet on nVidia's TitanX GPU.
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Inspired by the popular deep learning architecture - Deep Stacking Network (DSN), a specific deep model for polarimetric synthetic aperture radar (POLSAR) image classification is proposed in this paper, which is named as Wishart Deep Stacking Network (W-DSN). First of all, a fast implementation of Wishart distance is achieved by a special linear transformation, which speeds up the classification of POLSAR image and makes it possible to use this polarimetric information in the following Neural Network (NN). Then a single-hidden-layer neural network based on the fast Wishart distance is defined for POLSAR image classification, which is named as Wishart Network (WN) and improves the classification accuracy. Finally, a multi-layer neural network is formed by stacking WNs, which is in fact the proposed deep learning architecture W-DSN for POLSAR image classification and improves the classification accuracy further. In addition, the structure of WN can be expanded in a straightforward way by adding hidden units if necessary, as well as the structure of the W-DSN. As a preliminary exploration on formulating specific deep learning architecture for POLSAR image classification, the proposed methods may establish a simple but clever connection between POLSAR image interpretation and deep learning. The experiment results tested on real POLSAR image show that the fast implementation of Wishart distance is very efficient (a POLSAR image with 768000 pixels can be classified in 0.53s), and both the single-hidden-layer architecture WN and the deep learning architecture W-DSN for POLSAR image classification perform well and work efficiently.
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Deep Learning algorithm is widely used for various pattern recognition applications such as text recognition, object recognition and action recognition because of its best-in-class recognition accuracy compared to hand-crafted algorithm and shallow learning based algorithms. Long learning time caused by its complex structure, however, limits its usage only in high-cost servers or many-core GPU platforms so far. On the other hand, the demand on customized pattern recognition within personal devices will grow gradually as more deep learning applications will be developed. This paper presents a SoC implementation to enable deep learning applications to run with low cost platforms such as mobile or portable devices. Different from conventional works which have adopted massively-parallel architecture, this work adopts task-flexible architecture and exploits multiple parallelism to cover complex functions of convolutional deep belief network which is one of popular deep learning/inference algorithms. In this paper, we implement the most energy-efficient deep learning and inference processor for wearable system. The implemented 2.5 mm ×4.0 mm deep learning/inference processor is fabricated using 65 nm 8-metal CMOS technology for a battery-powered platform with real-time deep inference and deep learning operation. It consumes 185 mW average power, and 213.1 mW peak power at 200 MHz operating frequency and 1.2 V supply voltage. It achieves 411.3 GOPS peak performance and 1.93 TOPS/W energy efficiency, which is 2.07× higher than the state-of-the-art.
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Convolutional Neural Networks (CNNs) have been successfully used for many computer vision applications. It would be beneficial to these applications if the computational workload of CNNs could be reduced. In this work we analyze the linear algebraic properties of CNNs and propose an algorithmic modification to reduce their computational workload. An up to a 47% reduction can be achieved without any change in the image recognition results or the addition of any hardware accelerators.
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Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
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This letter presents a rotation-invariant method for detecting geospatial objects from high-resolution satellite images. First, a superpixel segmentation strategy is proposed to generate meaningful and nonredundant patches. Second, a multilayer deep feature generation model is developed to generate high-level feature representations of patches using deep learning techniques. Third, a set of multiscale Hough forests with embedded patch orientations is constructed to cast rotation-invariant votes for estimating object centroids. Quantitative evaluations on the images collected from Google Earth service show that an average completeness, correctness, quality, and F1- measure values of 0.958, 0.969, 0.929, and 0.963, respectively, are obtained. Comparative studies with three existing methods demonstrate the superior performance of the proposed method in accurately and correctly detecting objects that are arbitrarily oriented and of varying sizes.
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We present YOLO, a unified pipeline for object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is also extremely fast; YOLO processes images in real-time at 45 frames per second, hundreds to thousands of times faster than existing detection systems. Our system uses global image context to detect and localize objects, making it less prone to background errors than top detection systems like R-CNN. By itself, YOLO detects objects at unprecedented speeds with moderate accuracy. When combined with state-of-the-art detectors, YOLO boosts performance by 2-3% points mAP.
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The hybrid deep neural network (DNN) and hidden Markov model (HMM) has recently achieved dramatic performance gains in automatic speech recognition (ASR). The DNN-based acoustic model is very powerful but its learning process is extremely time-consuming. In this paper, we propose a novel DNN-based acoustic modeling framework for speech recognition, where the posterior probabilities of HMM states are computed from multiple DNNs (mDNN), instead of a single large DNN, for the purpose of parallel training towards faster turnaround. In the proposed mDNN method all tied HMM states are first grouped into several disjoint clusters based on data-driven methods. Next, several hierarchically structured DNNs are trained separately in parallel for these clusters using multiple computing units (e.g. GPUs). In decoding, the posterior probabilities of HMM states can be calculated by combining outputs from multiple DNNs. In this work, we have shown that the training procedure of the mDNN under popular criteria, including both frame-level cross-entropy and sequence-level discriminative training, can be parallelized efficiently to yield significant speedup. The training speedup is mainly attributed to the fact that multiple DNNs are parallelized over multiple GPUs and each DNN is smaller in size and trained by only a subset of training data. We have evaluated the proposed mDNN method on a 64-hour Mandarin transcription task and the 320-hour Switchboard task. Compared to the conventional DNN, a 4-cluster mDNN model with similar size can yield comparable recognition performance in Switchboard (only about 2% performance degradation) with a greater than 7 times speed improvement in CE training and a 2.9 times improvement in sequence training, when 4 GPUs are used.
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Detection of salient objects from images is gaining increasing research interest in recent years as it can substantially facilitate a wide range of content-based multimedia applications. Based on the assumption that foreground salient regions are distinctive within a certain context, most conventional approaches rely on a number of hand-designed features and their distinctiveness is measured using local or global contrast. Although these approaches have been shown to be effective in dealing with simple images, their limited capability may cause difficulties when dealing with more complicated images. This paper proposes a novel framework for saliency detection by first modeling the background and then separating salient objects from the background. We develop stacked denoising autoencoders with deep learning architectures to model the background where latent patterns are explored and more powerful representations of data are learned in an unsupervised and bottom-up manner. Afterward, we formulate the separation of salient objects from the background as a problem of measuring reconstruction residuals of deep autoencoders. Comprehensive evaluations of three benchmark datasets and comparisons with nine state-of-the-art algorithms demonstrate the superiority of this paper.
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Most modern convolutional neural networks (CNNs) used for object recognition are built using the same principles: Alternating convolution and max-pooling layers followed by a small number of fully connected layers. We re-evaluate the state of the art for object recognition from small images with convolutional networks, questioning the necessity of different components in the pipeline. We find that max-pooling can simply be replaced by a convolutional layer with increased stride without loss in accuracy on several image recognition benchmarks. Following this finding -- and building on other recent work for finding simple network structures -- we propose a new architecture that consists solely of convolutional layers and yields competitive or state of the art performance on several object recognition datasets (CIFAR-10, CIFAR-100, ImageNet). To analyze the network we introduce a new variant of the "deconvolution approach" for visualizing features learned by CNNs, which can be applied to a broader range of network structures than existing approaches.
Searching for MobileNetV3
• A Howard
A. Howard et al., "Searching for MobileNetV3," 2019, arXiv:1905.02244. [Online]. Available: https://arxiv.org/abs/1905.02244
• M Sandler
• A Howard
• M Zhu
• A Zhmoginov
• L.-C Chen
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, "MobileNetV2: Inverted residuals and linear bottlenecks," 2018, arXiv:1801.04381. [Online]. Available: https://arxiv.org/abs/1801.04381