J.M. Rabaey

J.M. Rabaey
University of California, Berkeley | UCB · Department of Electrical Engineering and Computer Sciences

PhD

About

535
Publications
70,878
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
34,986
Citations
Citations since 2016
61 Research Items
8661 Citations
201620172018201920202021202202004006008001,0001,200
201620172018201920202021202202004006008001,0001,200
201620172018201920202021202202004006008001,0001,200
201620172018201920202021202202004006008001,0001,200
Additional affiliations
May 1987 - July 2014
University of California, Berkeley
Position
  • Donald O. Pederson Distinguished Professor

Publications

Publications (535)
Article
Full-text available
In this paper, a hardware-optimized approach to emotion recognition based on the efficient brain-inspired hyperdimensional computing (HDC) paradigm is proposed. Emotion recognition provides valuable information for human–computer interactions; however, the large number of input channels (> 200) and modalities (> 3 ) involved in emotion recognition...
Article
This article reviews recent progress in the development of the computing framework vector symbolic architectures (VSA) (also known as hyperdimensional computing). This framework is well suited for implementation in stochastic, emerging hardware, and it naturally expresses the types of cognitive operations required for artificial intelligence (AI)....
Preprint
Full-text available
We introduce a method to identify speakers by computing with high-dimensional random vectors. Its strengths are simplicity and speed. With only 1.02k active parameters and a 128-minute pass through the training data we achieve Top-1 and Top-5 scores of 31% and 52% on the VoxCeleb1 dataset of 1,251 speakers. This is in contrast to CNN models requiri...
Article
Hyperdimensional computing (HDC) is a brain-inspired computing paradigm that operates on pseudo-random hypervectors to perform high-accuracy classifications for biomedical applications. The energy efficiency of prior HDC processors for this computationally minimal algorithm is dominated by costly hypervector memory storage, which grows linearly wit...
Article
Surface-mounted parallel-plate capacitors (PPCs) can be used as transducing elements in wireless communication, for example, in human-body-dedicated applications, such as healthcare monitoring or activity tracking, which require robust wireless communications and user-convenient implementation. In this context, PPCs are an attractive element due to...
Article
Traditional methods for radio frequency (RF) beamforming require significant amounts of power, making them difficult to deploy in some low-power applications. A detailed analysis of RF beamforming utilizing transmission-line transformers (TLTs) and balanced impedance phase shifters (BIPSs) is presented. This technique enables ultralow-power RF beam...
Article
Memory-augmented neural networks enhance a neural network with an external key-value (KV) memory whose complexity is typically dominated by the number of support vectors in the key memory. We propose a generalized KV memory that decouples its dimension from the number of support vectors by introducing a free parameter that can arbitrarily add or re...
Preprint
Full-text available
Memory-augmented neural networks enhance a neural network with an external key-value memory whose complexity is typically dominated by the number of support vectors in the key memory. We propose a generalized key-value memory that decouples its dimension from the number of support vectors by introducing a free parameter that can arbitrarily add or...
Preprint
Full-text available
Semiconductor innovation drives improvements to technologies that are critical to modern society. The country that successfully accelerates semiconductor innovation is positioned to lead future semiconductor-driven industries and benefit from the resulting economic growth. It is our view that a next generation infrastructure is necessary to acceler...
Preprint
div>Prosthetic control for rehabilitation, among many other applications, can leverage in-sensor hand gesture recognition in which lightweight machine learning models for classifying electromyogram (EMG) signals are embedded on miniature, low-power devices. While research efforts have demonstrated high accuracy in controlled settings, these methods...
Preprint
div>Prosthetic control for rehabilitation, among many other applications, can leverage in-sensor hand gesture recognition in which lightweight machine learning models for classifying electromyogram (EMG) signals are embedded on miniature, low-power devices. While research efforts have demonstrated high accuracy in controlled settings, these methods...
Conference Paper
Full-text available
Machine learning algorithms deployed on edge devices must meet certain resource constraints and efficiency requirements. Random Vector Functional Link (RVFL) networks are favored for such applications due to their simple design and training efficiency. We propose a modified RVFL network that avoids computationally expensive matrix operations during...
Preprint
Full-text available
Machine learning algorithms deployed on edge devices must meet certain resource constraints and efficiency requirements. Random Vector Functional Link (RVFL) networks are favored for such applications due to their simple design and training efficiency. We propose a modified RVFL network that avoids computationally expensive matrix operations during...
Preprint
Full-text available
This article reviews recent progress in the development of the computing framework Vector Symbolic Architectures (also known as Hyperdimensional Computing). This framework is well suited for implementation in stochastic, nanoscale hardware and it naturally expresses the types of cognitive operations required for Artificial Intelligence (AI). We dem...
Preprint
Full-text available
In this paper, a hardware-optimized approach to emotion recognition based on the efficient brain-inspired hyperdimensional computing (HDC) paradigm is proposed. Emotion recognition provides valuable information for human-computer interactions, however the large number of input channels (>200) and modalities (>3) involved in emotion recognition are...
Article
Full-text available
Wireless body area networks (WBANs) present unique challenges due to their specific characteristics of mobility and over-the-body radio propagation. A huge amount of factors—both internal and external to the network—affect WBAN channel conditions, so a reliable and comprehensive theoretical model of these communications is unfeasible and impractica...
Preprint
Full-text available
Electromyogram (EMG) pattern recognition can be used to classify hand gestures and movements for human-machine interface and prosthetics applications, but it often faces reliability issues resulting from limb position change. One method to address this is dual-stage classification, in which the limb position is first determined using additional sen...
Article
Full-text available
Wearable devices that monitor muscle activity based on surface electromyography could be of use in the development of hand gesture recognition applications. Such devices typically use machine-learning models, either locally or externally, for gesture classification. However, most devices with local processing cannot offer training and updating of t...
Article
Full-text available
Sensor data can be wirelessly transmitted from simple, battery-less tags using Radio Frequency Identification (RFID). RFID sensor tags consist of an antenna, a radio frequency integrated circuit chip (RFIC), and at least one sensor. An ideal tag can communicate over a long distance and be seamlessly integrated onto everyday objects. However, miniat...
Article
Full-text available
Wireless Body Area Networks (WBANs) are a fast-growing field fueled by the number of wearable devices developed for countless applications appearing on the market. To enable communication between a variety of those devices, the IEEE 802.15.6 standard was established. However, this standard has some intrinsic limitations in addressing the heterogene...
Article
Full-text available
The increasing penetration of wearable and implantable devices necessitates energy-efficient and robust ways of connecting them to each other and to the cloud. However, the wireless channel around the human body poses unique challenges such as a high and variable path-loss caused by frequent changes in the relative node positions as well as the sur...
Article
With the world around us rapidly becoming smarter, an extremely relevant question is how “we humans” are going to cope with the onslaught of information coming at us. One plausible answer is to use similar technologies to evolve ourselves and to equip us with the necessary tools to interact with and to become an essential part of the smart world. V...
Chapter
One viable solution for continuous reduction in energy-per-operation is to rethink functionality to cope with uncertainty by adopting computational approaches that are inherently robust to uncertainty. It requires a novel look at data representations, associated operations, and circuits, and at materials and substrates that enable them. 3D integrat...
Article
Full-text available
Closed-loop neuromodulation systems aim to treat a variety of neurological conditions by delivering and adjusting therapeutic electrical stimulation in response to a patient’s neural state, recorded in real time. Existing systems are limited by low channel counts, lack of algorithmic flexibility, and the distortion of recorded signals by large and...
Preprint
Accurate recognition of hand gestures is crucial to the functionality of smart prosthetics and other modern human-computer interfaces. Many machine learning-based classifiers use electromyography (EMG) signals as input features, but they often misclassify gestures performed in different situational contexts (changing arm position, reapplication of...
Preprint
Full-text available
One viable solution for continuous reduction in energy-per-operation is to rethink functionality to cope with uncertainty by adopting computational approaches that are inherently robust to uncertainty. It requires a novel look at data representations, associated operations, and circuits, and at materials and substrates that enable them. 3D integrat...
Article
Full-text available
This paper investigates the potential of using spray coating as a methodology for flexible antenna fabrication. The methodology has advantages compared to other antenna-printing techniques such as screen-printing and gravure printing (more flexibility in design), or inkjet printing (faster production). The methodology is demonstrated using two diff...
Article
Recognizing the very size of the brain’s circuits, hyperdimensional (HD) computing can model neural activity patterns with points in a HD space, that is, with HD vectors. Key examined properties of HD computing include: a versatile set of arithmetic operations on HD vectors, generality, scalability, analyzability, one-shot learning, and energy effi...
Article
The field of machine learning is witnessing rapid advances along several fronts: new machine learning models, new machine learning algorithms utilizing these models, new hardware architectures for these algorithms, and new technologies for creating energy-efficient implementations of such hardware architectures. Hyperdimensional (HD) computing repr...
Article
We present a 65-nm CMOS neuromodulation SoC with a stimulus artifact canceler allowing simultaneous recording and stimulation. A first-order ΔΣ-modulated switched capacitor DAC injects artifact canceling charges at the LNA input, handling up to 200 mV <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pp<...
Preprint
The Human Intranet is envisioned as an open, scalable platform that seamlessly integrates an ever-increasing number of sensor, actuation, computation, storage, communication, and energy nodes located on, in, or around the human body, acting in symbiosis with the functions provided by the body itself. The limited amount of available energy and the c...
Article
An ultra-wideband (UWB)-based cognitive radio (CR) is a promising technique to utilize 3.1-10.6-GHz band efficiently for high data-rate short-range wireless connectivity even in overcrowdedness of the frequency spectrum. Frequency synthesizers tailored to the UWB-based CR should provide wide lock range, multiple frequency bands, and low in-band spu...
Article
The emergence of post-silicon nano-devices and the coming trillion-sensor era driven by the Internet of Things (IoT) have led to a search for alternative computational paradigms that can efficiently derive useful information from abundant data while enable efficient hardware implementations under significant device variations. Such computational pa...
Article
Full-text available
Hyperdimensional (HD) computing is a promising paradigm for future intelligent electronic appliances operating at low power. This paper discusses tradeoffs of selecting parameters of binary HD representations when applied to pattern recognition tasks. Particular design choices include density of representations and strategies for mapping data from...
Conference Paper
Conference Paper
EMG-based gesture recognition shows promise for human-machine interaction. Systems are often afflicted by signal and electrode variability which degrades performance over time. We present an end-to-end system combating this variability using a large-area, high-density sensor array and a robust classification algorithm. EMG electrodes are fabricated...
Article
An ultra-wideband (UWB)-based cognitive radio (CR) is a promising technique to utilize 3.1-10.6 GHz band efficiently for high data-rate short-range wireless connectivity even in the overcrowded spectrum. This paper investigates a low power wavelet-based energy detection spectrum sensor that provides high-order band-pass filter function and spectral...
Conference Paper
Full-text available
Location information of mobile devices is a foun-dational input to location-based services and a valuable source of context information in wireless networks. To maximize the value, we need location information that is accurate, robust, and promptly and seamlessly available. Unfortunately, individual localization services seldom satisfy all these re...
Article
Closed-loop neuromodulation systems aim to treat a variety of neurological conditions by dynamically delivering and adjusting therapeutic electrical stimulation in response to the neural state of a patient. Closed-loop devices must simultaneously record neural signals, remove stimulation artifact from recorded data, and extract neural biomarkers to...
Conference Paper
We address the design space exploration of wireless body area networks for wearable and implantable technologies, a task that is increasingly challenging as the number and variety of devices per person grow. Our method efficiently decomposes the problem into smaller subproblems by coordinating specialized analysis and optimization techniques. We le...
Article
This review focuses on recent directions stemming from work by the authors and collaborators in the emerging field of neurotechnology. Neurotechnology has the potential to provide a greater understanding of the structure and function of the complex neural circuits in the brain, as well as impacting the field of brain–machine interfaces (BMI). We en...
Conference Paper
Full-text available
Localization services available on today's mobile devices are proprietary and leverage a limited set of sources of location information. Integration of new location estimation methods is therefore cumbersome, requiring adaptation to the specific interfaces of the proprietary location service. In addition , location-based applications are tightly in...
Article
The emerging field of bioelectronic medicine seeks methods for deciphering and modulating electrophysiological activity in the body to attain therapeutic effects at target organs. Current approaches to interfacing with peripheral nerves and muscles rely heavily on wires, creating problems for chronic use, while emerging wireless approaches lack the...
Conference Paper
A distributed, modular, intelligent, and efficient neuromodulation device, called OMNI, is presented. It supports closed-loop recording and stimulation on 256 channels from up to 4 physically distinct neuromodulation modules placed in any configuration around the brain, hence offering the capability of addressing neural disorders that are presented...
Conference Paper
In this paper, we present an ultrasonic beamforming system capable of interrogating individual implantable sensors via backscatter in a distributed, ultrasound-based recording platform known as Neural Dust [1]. A custom ASIC drives a 7 × 2 PZT transducer array with 3 cycles of 32V square wave with a specific programmable time delay to focus the bea...
Conference Paper
A triple-channel BPSK UWB-based cognitive radio provides energy efficient 1Gb/s short-range connectivity by scavenging triple discrete inactive frequency bands in 3.1–10.6GHz ISM bands. The developed transceiver in 65nm CMOS achieves the minimum energy consumption of 59.7pJ/b with 1.97×10 −4 BER. Die area is 4.6mm 2 with on-die PLLs.
Article
A 65 nm CMOS 4.78 mm 2 integrated neuromodulation SoC consumes 348 µA from an unregulated 1.2 V to 1.8 V supply while operating 64 acquisition channels with epoch compression at an average firing rate of 50 Hz and engaging two stimulators with a pulse width of 250 µs/phase, differential current of 150 µA, and a pulse frequency of 100 Hz. Compared t...
Article
Brain-machine interface (BMI) technology has tremendous potential to revolutionize healthcare by greatly im-proving the quality of life of millions of people suffering from a wide variety of neurological conditions. Radio-frequency identi-fication (RFID)-inspired backscattering is a promising approach for wireless powering of miniature neural senso...
Article
Emerging applications in brain-machine interface systems require high-resolution, chronic multisite cortical recordings, which cannot be obtained with existing technologies due to high power consumption, high invasiveness, or inability to transmit data wirelessly. In this paper, we describe a microsystem based on electrocorticography (ECoG) that ov...
Chapter
Fritsch and Hitzig first discovered the motor cortex in 1870 [1], although the best-known experimentalmapping of the motor cortex dates back to Penfield’s experiments in 1937 [2] using electrical stimulation to activate muscle groups in patients undergoing surgery for epilepsy. It was not until the 1980s, over 100 years since the discovery of the m...