98 citations · 270 across the 20 of their papers we have counts for
5 papers · 1 filter
Mixed-Signal Charge-Domain Acceleration of Deep Neural networks through Interleaved Bit-Partitioned Arithmetic
Soroush Ghodrati, Hardik Sharma, Sean Kinzer +5
Low-power potential of mixed-signal design makes it an alluring option to accelerate Deep Neural Networks (DNNs). However, mixed-signal circuitry suffers from limited range for inf…
Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks
Ahmed T. Elthakeb, Prannoy Pilligundla, Alex Cloninger +1
The deep layers of modern neural networks extract a rather rich set of features as an input propagates through the network. This paper sets out to harvest these rich intermediate r…
Reinforcement Learning and Adaptive Sampling for Optimized DNN Compilation
Byung Hoon Ahn, Prannoy Pilligundla, Hadi Esmaeilzadeh
Achieving faster execution with shorter compilation time can enable further diversity and innovation in neural networks. However, the current paradigm of executing neural networks…
Shredder: Learning Noise Distributions to Protect Inference Privacy
Fatemehsadat Mireshghallah, Mohammadkazem Taram, Prakash Ramrakhyani +2
A wide variety of deep neural applications increasingly rely on the cloud to perform their compute-heavy inference. This common practice requires sending private and privileged dat…
SinReQ: Generalized Sinusoidal Regularization for Low-Bitwidth Deep Quantized Training
Ahmed T. Elthakeb, Prannoy Pilligundla, Hadi Esmaeilzadeh
Deep quantization of neural networks (below eight bits) offers significant promise in reducing their compute and storage cost. Albeit alluring, without special techniques for train…