45 citations · 86 across the 25 of their papers we have counts for
6 papers · 1 filter
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…
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…