1 citations · 1 across the 3 of their papers we have counts for
3 papers
MXFormer: A Microscaling Floating-Point Charge-Trap Transistor Compute-in-Memory Transformer Accelerator
George Karfakis, Samyak Chakrabarty, Vinod Kurian Jacob +4
The proliferation of Transformer models is often constrained by the significant computational and memory bandwidth demands of deployment. To address this, we present MXFormer, a no…
System-level Impact of Non-Ideal Program-Time of Charge Trap Flash (CTF) on Deep Neural Network
S. Shrivastava, A. Biswas, S. Chakrabarty +3
Learning of deep neural networks (DNN) using Resistive Processing Unit (RPU) architecture is energy-efficient as it utilizes dedicated neuromorphic hardware and stochastic computat…
Non-Ideal Program-Time Conservation in Charge Trap Flash for Deep Learning
Shalini Shrivastava, Vivek Saraswat, Gayatri Dash +2
Training deep neural networks (DNNs) is computationally intensive but arrays of non-volatile memories like Charge Trap Flash (CTF) can accelerate DNN operations using in-memory com…