45 citations · 214 across the 86 of their papers we have counts for
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Hardware Trojan Threats to Multi-Chiplet Photonic Neural Network Accelerators
Sudeep Pasricha
Multi-chiplet photonic neural network accelerators (MCPNAs) combine photonic computation, photonic communica-tion, and heterogeneous chiplet integration to enable scalable and ener…
MCFlash: Bulk Bitwise Processing in 3D NAND with Dynamic Sensing and Multi-level Encoding
Habib Ur Rahman, Tharini Suresh, Sudeep Pasricha +1
This paper presents MCFlash, a practical and immediately deployable technique for executing bulk bitwise operations directly within commercial off-the-shelf(COTS) 3D NAND flash chi…
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…
Accelerating Diffusion Models for Generative AI Applications with Silicon Photonics
Tharini Suresh, Salma Afifi, Sudeep Pasricha
Diffusion models have revolutionized generative AI, with their inherent capacity to generate highly realistic state-of-the-art synthetic data. However, these models employ an itera…
PhotoGAN: Generative Adversarial Neural Network Acceleration with Silicon Photonics
Tharini Suresh, Salma Afifi, Sudeep Pasricha
Generative Adversarial Networks (GANs) are at the forefront of AI innovation, driving advancements in areas such as image synthesis, medical imaging, and data augmentation. However…
Optical Computing for Deep Neural Network Acceleration: Foundations, Recent Developments, and Emerging Directions
Sudeep Pasricha
Emerging artificial intelligence applications across the domains of computer vision, natural language processing, graph processing, and sequence prediction increasingly rely on dee…
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…
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…
Electronic, Wireless, and Photonic Network-on-Chip Security: Challenges and Countermeasures
Sudeep Pasricha, John Jose, Sujay Deb
Networks-on-chips (NoCs) are an integral part of emerging manycore computing chips. They play a key role in facilitating communication among processing cores and between cores and…
ReSiPI: A Reconfigurable Silicon-Photonic 2.5D Chiplet Network with PCMs for Energy-Efficient Interposer Communication
Ebadollah Taheri, Sudeep Pasricha, Mahdi Nikdast
2.5D chiplet systems have been proposed to improve the low manufacturing yield of large-scale chips. However, connecting the chiplets through an electronic interposer imposes a hig…
RACE: A Reinforcement Learning Framework for Improved Adaptive Control of NoC Channel Buffers
Kamil Khan, Sudeep Pasricha, Ryan Gary Kim
Network-on-chip (NoC) architectures rely on buffers to store flits to cope with contention for router resources during packet switching. Recently, reversible multi-function channel…
A Silicon Photonic Accelerator for Convolutional Neural Networks with Heterogeneous Quantization
Febin Sunny, Mahdi Nikdast, Sudeep Pasricha
Parameter quantization in convolutional neural networks (CNNs) can help generate efficient models with lower memory footprint and computational complexity. But, homogeneous quantiz…
A Survey of Resource Management for Processing-in-Memory and Near-Memory Processing Architectures
Kamil Khan, Sudeep Pasricha, Ryan Gary Kim
Due to amount of data involved in emerging deep learning and big data applications, operations related to data movement have quickly become the bottleneck. Data-centric computing (…
Exploiting Process Variations to Secure Photonic NoC Architectures from Snooping Attacks
Sai Vineel Reddy Chittamuru, Ishan G Thakkar, Sudeep Pasricha +2
The compact size and high wavelength-selectivity of microring resonators (MRs) enable photonic networks-on-chip (PNoCs) to utilize dense-wavelength-division-multiplexing (DWDM) in…
LORAX: Loss-Aware Approximations for Energy-Efficient Silicon Photonic Networks-on-Chip
Febin Sunny, Asif Mirza, Ishan Thakkar +2
The approximate computing paradigm advocates for relaxing accuracy goals in applications to improve energy-efficiency and performance. Recently, this paradigm has been explored to…