Charlotte Frenkel

Charlotte Frenkel
Delft University of Technology | TU

About

32
Publications
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487
Citations

Publications

Publications (32)
Preprint
In this paper, we reviewed Spiking neural network (SNN) integrated circuit designs and analyzed the trends among mixed-signal cores, fully digital cores and large-scale, multi-core designs. Recently reported SNN integrated circuits are compared under three broad categories: (a) Large-scale multi-core designs that have dedicated NOC for spike routin...
Conference Paper
A robust real-world deployment of autonomous edge devices requires on-chip adaptation to user-, environment- and task-induced variability. Due to on-chip memory constraints, prior learning devices were limited to static stimuli with no temporal contents. We propose a 0.45-mm$^2$ spiking RNN processor enabling task-agnostic online learning over seco...
Preprint
Full-text available
Novel non-volatile memory devices based on ferroelectric thin films represent a promising emerging technology that is ideally suited for neuromorphic applications. The physical switching mechanism in such films is the nucleation and growth of ferroelectric domains. Since this has a strong dependence on both pulse width and voltage amplitude, it is...
Preprint
Full-text available
The stringent memory and power constraints required in edge-computing sensory-processing applications have made event-driven neuromorphic systems a promising technology. On-chip online learning provides such systems the ability to learn the statistics of the incoming data and to adapt to their changes. Implementing online learning on event driven-n...
Article
Neuromorphic chips that use spikes to encode information could provide fast and energy-efficient computing for ubiquitous embedded systems. A bio-plausible spike-timing solution for training spiking neural networks that makes the most of sparsity is implemented on the BrainScaleS-2 hardware platform.
Preprint
Full-text available
Spiking recurrent neural networks (RNNs) are a promising tool for solving a wide variety of complex cognitive and motor tasks, due to their rich temporal dynamics and sparse processing. However training spiking RNNs on dedicated neuromorphic hardware is still an open challenge. This is due mainly to the lack of local, hardware-friendly learning mec...
Preprint
Full-text available
While Moore's law has driven exponential computing power expectations, its nearing end calls for new avenues for improving the overall system performance. One of these avenues is the exploration of new alternative brain-inspired computing architectures that promise to achieve the flexibility and computational efficiency of biological neural process...
Preprint
While Moore's law has driven exponential computing power expectations, its nearing end calls for new avenues for improving the overall system performance. One of these avenues is the exploration of new alternative brain-inspired computing architectures that promise to achieve the flexibility and computational efficiency of biological neural process...
Conference Paper
Full-text available
Dedicated hardware implementations of spiking neural networks that combine the advantages of mixed-signal neuromorphic circuits with those of emerging memory technologies have the potential of enabling ultra-low power pervasive sensory processing. To endow these systems with additional flexibility and the ability to learn to solve specific tasks, i...
Article
Preventing device obsolescence in Internet-of-things (IoT) is mandatory for its massive deployment to be ecologically sustainable. This calls for ultralow-power (ULP) reprogrammable microcontroller units (MCUs) for long lifetime, yet with sufficient computing performance to extract the meaningful information from the sensed data before transmitting...
Preprint
Full-text available
Dedicated hardware implementations of spiking neural networks that combine the advantages of mixed-signal neuromorphic circuits with those of emerging memory technologies have the potential of enabling ultra-low power pervasive sensory processing. To endow these systems with additional flexibility and the ability to learn to solve specific tasks, i...
Article
Full-text available
While the backpropagation of error algorithm enables deep neural network training, it implies (i) bidirectional synaptic weight transport and (ii) update locking until the forward and backward passes are completed. Not only do these constraints preclude biological plausibility, but they also hinder the development of low-cost adaptive smart sensors...
Article
Spiking recurrent neural networks (RNNs) are a promising tool for solving a wide variety of complex cognitive and motor tasks, due to their rich temporal dynamics and sparse processing. However training spiking RNNs on dedicated neuromorphic hardware is still an open challenge. This is due mainly to the lack of local, hardware-friendly learning mec...
Conference Paper
In an attempt to follow biological information representation and organization principles, the field of neuromorphic engineering is usually approached bottom-up, from the biophysical models to large-scale integration in silico. While ideal as experimentation platforms for cognitive computing and neuroscience, bottom-up neuromorphic processors have...
Preprint
Full-text available
In an attempt to follow biological information representation and organization principles, the field of neuromorphic engineering is usually approached bottom-up, from the biophysical models to large-scale integration in silico. While ideal as experimentation platforms for cognitive computing and neuroscience, bottom-up neuromorphic processors have...
Article
Full-text available
Hand gestures are a form of non-verbal communication used by individuals in conjunction with speech to communicate. Nowadays, with the increasing use of technology, hand-gesture recognition is considered to be an important aspect of Human-Machine Interaction (HMI), allowing the machine to capture and interpret the user's intent and to respond accor...
Thesis
Full-text available
While Moore's law has driven exponential computing power expectations, its nearing end calls for new roads to embedded cognition. The field of neuromorphic computing aims at a two-fold paradigm shift compared to conventional computing. First, it investigates the co-location of processing and memory in neurons and synapses. Second, it aims at encodi...
Article
Chip-to-chip communications in high-performance applications such as server racks rely on wireline serial links. In this paper, we present a compact ultra-wideband receiver front-end in 28-nm FDSOI CMOS technology as a wireless interconnect alternative for low-energy and broadcast communications. It is based on binary pulse position modulation with...
Article
Recent trends in the field of neural network accelerators investigate weight quantization as a means to increase the resource- and power-efficiency of hardware devices. As full on-chip weight storage is necessary to avoid the high energy cost of off-chip memory accesses, memory reduction requirements for weight storage pushed toward the use of bina...
Preprint
Full-text available
While the backpropagation of error algorithm allowed for a rapid rise in the development and deployment of artificial neural networks, two key issues currently preclude biological plausibility: (i) symmetry is required between forward and backward weights, which is known as the weight transport problem, and (ii) updates are locked before both the f...
Preprint
Recent trends in the field of neural network accelerators investigate weight quantization as a means to increase the resource- and power-efficiency of hardware devices. As full on-chip weight storage is necessary to avoid the high energy cost of off-chip memory accesses, memory reduction requirements for weight storage pushed toward the use of bina...
Preprint
Recent trends in the field of neural network accelerators investigate weight quantization as a means to increase the resource- and power-efficiency of hardware devices. As full on-chip weight storage is necessary to avoid the high energy cost of off-chip memory accesses, memory reduction requirements for weight storage pushed toward the use of bina...
Article
Shifting computing architectures from von Neumann to event-based spiking neural networks (SNNs) uncovers new opportunities for low-power processing of sensory data in applications such as vision or sensorimotor control. Exploring roads toward cognitive SNNs requires the design of compact, low-power and versatile experimentation platforms with the k...
Article
Full-text available
Achieving wireless communications at 5-30 Mb/s in energy-harvesting Internet-of-Things (IoT) applications requires energy efficiencies better than 100 pJ/b. Impulse-radio ultrawideband (UWB) communications offer an efficient way to achieve high data rate at ultralow power for short-range links. We propose a digital UWB transmitter (TX) system-on-ch...
Conference Paper
Single-Event Effects are an increasingly important issue in electronic circuits due to technology scaling, efficient error detection schemes are thus required for circuits dedicated to radiative environments, such as in space applications. This work shows that the widespread spatial and temporal redundancy schemes exhibit widely different performan...