12 citations · 82 across the 50 of their papers we have counts for
6 papers · 1 filter
Bitwidth-Specific Logarithmic Arithmetic for Future Hardware-Accelerated Training
Hassan Hamad, Yuou Qiu, Peter A. Beerel +1
While advancements in quantization have significantly reduced the computational costs of inference in deep learning, training still predominantly relies on complex floating-point a…
Linearizing Models for Efficient yet Robust Private Inference
Sreetama Sarkar, Souvik Kundu, Peter A. Beerel
The growing concern about data privacy has led to the development of private inference (PI) frameworks in client-server applications which protects both data privacy and model IP.…
Making Models Shallow Again: Jointly Learning to Reduce Non-Linearity and Depth for Latency-Efficient Private Inference
Souvik Kundu, Yuke Zhang, Dake Chen +1
Large number of ReLU and MAC operations of Deep neural networks make them ill-suited for latency and compute-efficient private inference. In this paper, we present a model optimiza…
P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications
Gourav Datta, Souvik Kundu, Zihan Yin +5
The demand to process vast amounts of data generated from state-of-the-art high resolution cameras has motivated novel energy-efficient on-device AI solutions. Visual data in such…
Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep Learning
Sourya Dey, Saikrishna C. Kanala, Keith M. Chugg +1
We present Deep-n-Cheap -- an open-source AutoML framework to search for deep learning models. This search includes both architecture and training hyperparameters, and supports con…
Morse Code Datasets for Machine Learning
Sourya Dey, Keith M. Chugg, Peter A. Beerel
We present an algorithm to generate synthetic datasets of tunable difficulty on classification of Morse code symbols for supervised machine learning problems, in particular, neural…