45 citations · 83 across the 7 of their papers we have counts for
3 papers · 1 filter
CiMLoop: A Flexible, Accurate, and Fast Compute-In-Memory Modeling Tool
Tanner Andrulis, Joel S. Emer, Vivienne Sze
Compute-In-Memory (CiM) is a promising solution to accelerate Deep Neural Networks (DNNs) as it can avoid energy-intensive DNN weight movement and use memory arrays to perform low-…
Architecture-Level Modeling of Photonic Deep Neural Network Accelerators
Tanner Andrulis, Gohar Irfan Chaudhry, Vinith M. Suriyakumar +2
Photonics is a promising technology to accelerate Deep Neural Networks as it can use optical interconnects to reduce data movement energy and it enables low-energy, high-throughput…
Modeling Analog-Digital-Converter Energy and Area for Compute-In-Memory Accelerator Design
Tanner Andrulis, Ruicong Chen, Hae-Seung Lee +2
Analog Compute-in-Memory (CiM) accelerators use analog-digital converters (ADCs) to read the analog values that they compute. ADCs can consume significant energy and area, so archi…