2 citations · 8 across the 30 of their papers we have counts for
7 papers · 1 filter
Mixed Delay/Nondelay Embeddings Based Neuromorphic Computing with Patterned Nanomagnet Arrays
Changpeng Ti, Usman Hassan, Sairam Sri Vatsavai +8
Patterned nanomagnet arrays (PNAs) have been shown to exhibit a strong geometrically frustrated dipole interaction. Some PNAs have also shown emergent domain wall dynamics. Previou…
SafeLight: Enhancing Security in Optical Convolutional Neural Network Accelerators
Salma Afifi, Ishan Thakkar, Sudeep Pasricha
The rapid proliferation of deep learning has revolutionized computing hardware, driving innovations to improve computationally expensive multiply-and-accumulate operations in deep…
ARTEMIS: A Mixed Analog-Stochastic In-DRAM Accelerator for Transformer Neural Networks
Salma Afifi, Ishan Thakkar, Sudeep Pasricha
Transformers have emerged as a powerful tool for natural language processing (NLP) and computer vision. Through the attention mechanism, these models have exhibited remarkable perf…
Scaling Analog Photonic Accelerators for Byte-Size, Integer General Matrix Multiply (GEMM) Kernels
Oluwaseun Adewunmi Alo, Sairam Sri Vatsavai, Ishan Thakkar
Deep Neural Networks (DNNs) predominantly rely on General Matrix Multiply (GEMM) kernels, which are often accelerated using specialized hardware architectures. Recently, analog pho…
A Low-Dissipation and Scalable GEMM Accelerator with Silicon Nitride Photonics
Venkata Sai Praneeth Karempudi, Sairam Sri Vatsavai, Ishan Thakkar +3
Over the past few years, several microring resonator (MRR)-based analog photonic architectures have been proposed to accelerate general matrix-matrix multiplications (GEMMs), which…
A Comparative Analysis of Microrings Based Incoherent Photonic GEMM Accelerators
Sairam Sri Vatsavai, Venkata Sai Praneeth Karempudi, Oluwaseun Adewunmi Alo +1
Several microring resonator (MRR) based analog photonic architectures have been proposed to accelerate general matrix-matrix multiplications (GEMMs) in deep neural networks with ex…