32 citations · 107 across the 32 of their papers we have counts for
9 papers · 1 filter
OPIMA: Optical Processing-In-Memory for Convolutional Neural Network Acceleration
Febin Sunny, Amin Shafiee, Abhishek Balasubramaniam +2
Recent advances in machine learning (ML) have spotlighted the pressing need for computing architectures that bridge the gap between memory bandwidth and processing power. The adven…
Silicon Photonic 2.5D Interposer Networks for Overcoming Communication Bottlenecks in Scale-out Machine Learning Hardware Accelerators
Febin Sunny, Ebadollah Taheri, Mahdi Nikdast +1
Modern machine learning (ML) applications are becoming increasingly complex and monolithic (single chip) accelerator architectures cannot keep up with their energy efficiency and t…
Accelerating Neural Networks for Large Language Models and Graph Processing with Silicon Photonics
Salma Afifi, Febin Sunny, Mahdi Nikdast +1
In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) and graph processing have emerged as transformative technologies for natural language pro…
COMET: A Cross-Layer Optimized Optical Phase Change Main Memory Architecture
Febin Sunny, Amin Shafiee, Benoit Charbonnier +2
Traditional DRAM-based main memory systems face several challenges with memory refresh overhead, high latency, and low throughput as the industry moves towards smaller DRAM cells.…
GHOST: A Graph Neural Network Accelerator using Silicon Photonics
Salma Afifi, Febin Sunny, Amin Shafiee +2
Graph neural networks (GNNs) have emerged as a powerful approach for modelling and learning from graph-structured data. Multiple fields have since benefitted enormously from the ca…
Machine Learning Accelerators in 2.5D Chiplet Platforms with Silicon Photonics
Febin Sunny, Ebadollah Taheri, Mahdi Nikdast +1
Domain-specific machine learning (ML) accelerators such as Google's TPU and Apple's Neural Engine now dominate CPUs and GPUs for energy-efficient ML processing. However, the evolut…