131 citations · 180 across the 5 of their papers we have counts for
5 papers
WWW: What, When, Where to Compute-in-Memory
Tanvi Sharma, Mustafa Ali, Indranil Chakraborty +1
Matrix multiplication is the dominant computation during Machine Learning (ML) inference. To efficiently perform such multiplication operations, Compute-in-memory (CiM) paradigms h…
LNS-Madam: Low-Precision Training in Logarithmic Number System using Multiplicative Weight Update
Jiawei Zhao, Steve Dai, Rangharajan Venkatesan +6
Representing deep neural networks (DNNs) in low-precision is a promising approach to enable efficient acceleration and memory reduction. Previous methods that train DNNs in low-pre…
IMPULSE: A 65nm Digital Compute-in-Memory Macro with Fused Weights and Membrane Potential for Spike-based Sequential Learning Tasks
Amogh Agrawal, Mustafa Ali, Minsuk Koo +3
The inherent dynamics of the neuron membrane potential in Spiking Neural Networks (SNNs) allows processing of sequential learning tasks, avoiding the complexity of recurrent neural…
PIM-DRAM: Accelerating Machine Learning Workloads using Processing in Commodity DRAM
Sourjya Roy, Mustafa Ali, Anand Raghunathan
Deep Neural Networks (DNNs) have transformed the field of machine learning and are widely deployed in many applications involving image, video, speech and natural language processi…
IMAC: In-memory multi-bit Multiplication andACcumulation in 6T SRAM Array
Mustafa Ali, Akhilesh Jaiswal, Sangamesh Kodge +3
`In-memory computing' is being widely explored as a novel computing paradigm to mitigate the well known memory bottleneck. This emerging paradigm aims at embedding some aspects of…