no code implementations • 9 Mar 2024 • Hanning Chen, Yang Ni, Ali Zakeri, Zhuowen Zou, Sanggeon Yun, Fei Wen, Behnam Khaleghi, Narayan Srinivasa, Hugo Latapie, Mohsen Imani
When conducting cross-models and cross-platforms comparison, HDReason yields an average 4. 2x higher performance and 3. 4x better energy efficiency with similar accuracy versus the state-of-the-art FPGA-based GCN training platform.
1 code implementation • 12 Jan 2024 • Mike Heddes, Narayan Srinivasa, Tony Givargis, Alexandru Nicolau
Sparsification of ANNs is often motivated by time, memory and energy savings only during model inference, yielding no benefits during training.
no code implementations • 11 Jan 2019 • Bipin Rajendran, Abu Sebastian, Michael Schmuker, Narayan Srinivasa, Evangelos Eleftheriou
In this paper, we review some of the architectural and system level design aspects involved in developing a new class of brain-inspired information processing engines that mimic the time-based information encoding and processing aspects of the brain.
no code implementations • 19 Oct 2017 • Priyadarshini Panda, Narayan Srinivasa
A fundamental challenge in machine learning today is to build a model that can learn from few examples.