4 papers
Space-CIM: Enabling Compute-In-Memory Accelerators for Thermally-Constrained Space Platforms
Sohan Salahuddin Mugdho, Md. Shahedul Hasan, Cheng Wang
The rapid growth in compute demand from artificial intelligence (AI) has driven a massive surge in data center construction, precipitating an energy and sustainability crisis. Moti…
CRAM-ER: Error-Resilient Spintronic Computational Random Access Memory for Scalable In-Memory Computation
Sohan Salahuddin Mugdho, Md. Shahedul Hasan, Brahmdutta Dixit +3
Deep neural networks (DNNs) have achieved state-of-the-art performance across diverse domains. However, typical Von Neumann compute paradigms face severe memory bottlenecks. Emergi…
Ultra-Efficient Decoding for End-to-End Neural Compression and Reconstruction
Ethan G. Rogers, Cheng Wang
Image compression and reconstruction are crucial for various digital applications. While contemporary neural compression methods achieve impressive compression rates, the adoption…
StoX-Net: Stochastic Processing of Partial Sums for Efficient In-Memory Computing DNN Accelerators
Ethan G Rogers, Sohan Salahuddin Mugdho, Kshemal Kshemendra Gupte +1
Crossbar-based in-memory computing (IMC) has emerged as a promising platform for hardware acceleration of deep neural networks (DNNs). However, the energy and latency of IMC system…