130 citations · 287 across the 16 of their papers we have counts for
5 papers · 1 filter
PANTHER: A Programmable Architecture for Neural Network Training Harnessing Energy-efficient ReRAM
Aayush Ankit, Izzat El Hajj, Sai Rahul Chalamalasetti +7
The wide adoption of deep neural networks has been accompanied by ever-increasing energy and performance demands due to the expensive nature of training them. Numerous special-purp…
PABO: Pseudo Agent-Based Multi-Objective Bayesian Hyperparameter Optimization for Efficient Neural Accelerator Design
Maryam Parsa, Aayush Ankit, Amirkoushyar Ziabari +1
The ever increasing computational cost of Deep Neural Networks (DNN) and the demand for energy efficient hardware for DNN acceleration has made accuracy and hardware cost co-optimi…
Constructing Energy-efficient Mixed-precision Neural Networks through Principal Component Analysis for Edge Intelligence
Indranil Chakraborty, Deboleena Roy, Isha Garg +2
The `Internet of Things' has brought increased demand for AI-based edge computing in applications ranging from healthcare monitoring systems to autonomous vehicles. Quantization is…
Efficient Hybrid Network Architectures for Extremely Quantized Neural Networks Enabling Intelligence at the Edge
Indranil Chakraborty, Deboleena Roy, Aayush Ankit +1
The recent advent of `Internet of Things' (IOT) has increased the demand for enabling AI-based edge computing. This has necessitated the search for efficient implementations of neu…
PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference
Aayush Ankit, Izzat El Hajj, Sai Rahul Chalamalasetti +8
Memristor crossbars are circuits capable of performing analog matrix-vector multiplications, overcoming the fundamental energy efficiency limitations of digital logic. They have be…