211 citations · 530 across the 27 of their papers we have counts for
29 papers
Scalable MatMul-free Language Modeling
Rui-Jie Zhu, Yu Zhang, Steven Abreu +7
Large Language Models (LLMs) have fundamentally altered how we approach scaling in machine learning. However, these models pose substantial computational and memory challenges, pri…
Autonomous Driving with Spiking Neural Networks
Rui-Jie Zhu, Ziqing Wang, Leilani Gilpin +1
Autonomous driving demands an integrated approach that encompasses perception, prediction, and planning, all while operating under strict energy constraints to enhance scalability…
Advancing Spiking Neural Networks towards Multiscale Spatiotemporal Interaction Learning
Yimeng Shan, Malu Zhang, Rui-jie Zhu +3
Recent advancements in neuroscience research have propelled the development of Spiking Neural Networks (SNNs), which not only have the potential to further advance neuroscience res…
SQUAT: Stateful Quantization-Aware Training in Recurrent Spiking Neural Networks
Sreyes Venkatesh, Razvan Marinescu, Jason K. Eshraghian
Weight quantization is used to deploy high-performance deep learning models on resource-limited hardware, enabling the use of low-precision integers for storage and computation. Sp…
Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing
Jens E. Pedersen, Steven Abreu, Matthias Jobst +12
Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine…
SynA-ResNet: Spike-driven ResNet Achieved through OR Residual Connection
Yimeng Shan, Xuerui Qiu, Rui-jie Zhu +3
Spiking Neural Networks (SNNs) have garnered substantial attention in brain-like computing for their biological fidelity and the capacity to execute energy-efficient spike-driven o…
Memristive Reservoirs Learn to Learn
Ruomin Zhu, Jason K. Eshraghian, Zdenka Kuncic
Memristive reservoirs draw inspiration from a novel class of neuromorphic hardware known as nanowire networks. These systems display emergent brain-like dynamics, with optimal perf…
To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Acceleration
Fabrizio Ottati, Chang Gao, Qinyu Chen +4
As deep learning models scale, they become increasingly competitive from domains spanning from computer vision to natural language processing; however, this happens at the expense…
PowerGAN: A Machine Learning Approach for Power Side-Channel Attack on Compute-in-Memory Accelerators
Ziyu Wang, Yuting Wu, Yongmo Park +4
Analog compute-in-memory (CIM) systems are promising for deep neural network (DNN) inference acceleration due to their energy efficiency and high throughput. However, as the use of…
OpenSpike: An OpenRAM SNN Accelerator
Farhad Modaresi, Matthew Guthaus, Jason K. Eshraghian
This paper presents a spiking neural network (SNN) accelerator made using fully open-source EDA tools, process design kit (PDK), and memory macros synthesized using OpenRAM. The ch…
SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks
Rui-Jie Zhu, Qihang Zhao, Guoqi Li +1
As the size of large language models continue to scale, so does the computational resources required to run it. Spiking Neural Networks (SNNs) have emerged as an energy-efficient a…
Intelligence Processing Units Accelerate Neuromorphic Learning
Pao-Sheng Vincent Sun, Alexander Titterton, Anjlee Gopiani +4
Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency when performing inference with deep learning workloads. Erro…
Spiking neural networks for nonlinear regression
Alexander Henkes, Jason K. Eshraghian, Henning Wessels
Spiking neural networks, also often referred to as the third generation of neural networks, carry the potential for a massive reduction in memory and energy consumption over tradit…
Side-channel attack analysis on in-memory computing architectures
Ziyu Wang, Fan-hsuan Meng, Yongmo Park +2
In-memory computing (IMC) systems have great potential for accelerating data-intensive tasks such as deep neural networks (DNNs). As DNN models are generally highly proprietary, th…
Gradient-based Neuromorphic Learning on Dynamical RRAM Arrays
Peng Zhou, Jason K. Eshraghian, Dong-Uk Choi +2
We present MEMprop, the adoption of gradient-based learning to train fully memristive spiking neural networks (MSNNs). Our approach harnesses intrinsic device dynamics to trigger n…
SPICEprop: Backpropagating Errors Through Memristive Spiking Neural Networks
Peng Zhou, Jason K. Eshraghian, Dong-Uk Choi +1
We present a fully memristive spiking neural network (MSNN) consisting of novel memristive neurons trained using the backpropagation through time (BPTT) learning rule. Gradient des…
A Fully Memristive Spiking Neural Network with Unsupervised Learning
Peng Zhou, Dong-Uk Choi, Jason K. Eshraghian +1
We present a fully memristive spiking neural network (MSNN) consisting of physically-realizable memristive neurons and memristive synapses to implement an unsupervised Spiking Time…
Navigating Local Minima in Quantized Spiking Neural Networks
Jason K. Eshraghian, Corey Lammie, Mostafa Rahimi Azghadi +1
Spiking and Quantized Neural Networks (NNs) are becoming exceedingly important for hyper-efficient implementations of Deep Learning (DL) algorithms. However, these networks face ch…
The fine line between dead neurons and sparsity in binarized spiking neural networks
Jason K. Eshraghian, Wei D. Lu
Spiking neural networks can compensate for quantization error by encoding information either in the temporal domain, or by processing discretized quantities in hidden states of hig…
Design Space Exploration of Dense and Sparse Mapping Schemes for RRAM Architectures
Corey Lammie, Jason K. Eshraghian, Chenqi Li +4
The impact of device and circuit-level effects in mixed-signal Resistive Random Access Memory (RRAM) accelerators typically manifest as performance degradation of Deep Learning (DL…
Training Spiking Neural Networks Using Lessons From Deep Learning
Jason K. Eshraghian, Max Ward, Emre Neftci +6
The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the futur…
FPGA Synthesis of Ternary Memristor-CMOS Decoders
Xiaoyuan Wang, Zhiru Wu, Pengfei Zhou +3
The search for a compatible application of memristor-CMOS logic gates has remained elusive, as the data density benefits are offset by slow switching speeds and resistive dissipati…
Memristive Stochastic Computing for Deep Learning Parameter Optimization
Corey Lammie, Jason K. Eshraghian, Wei D. Lu +1
Stochastic Computing (SC) is a computing paradigm that allows for the low-cost and low-power computation of various arithmetic operations using stochastic bit streams and digital l…
Naturalizing Neuromorphic Vision Event Streams Using GANs
Dennis Robey, Wesley Thio, Herbert Iu +1
Dynamic vision sensors are able to operate at high temporal resolutions within resource constrained environments, though at the expense of capturing static content. The sparse natu…