Publications (6)
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…
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…
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…
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…
Subgap pumping of antiferromagnetic Mott insulators: photoexcitation mechanisms and applications
Radu Andrei, Mingyao Guo, Mustafa Ali +4
We study the behavior of the 2D repulsive Hubbard model on a square lattice at half filling, under strong driving with ac electric fields, by employing a time-dependent Gaussian va…
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…