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Sustainable Transformer Neural Network Acceleration with Stochastic Photonic Computing
S. Afifi, O. Alo, I. Thakkar +1
Transformers achieve state-of-the-art performance in natural language processing, vision, and scientific computing, but demand high computation and memory. To address these challen…
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…
HEANA: A Hybrid Time-Amplitude Analog Optical Accelerator with Flexible Dataflows for Energy-Efficient CNN Inference
Sairam Sri Vatsavai, Venkata Sai Praneeth Karempudi, Ishan Thakkar
Several photonic microring resonators (MRRs) based analog accelerators have been proposed to accelerate the inference of integer-quantized CNNs with remarkably higher throughput an…