SS-L17.3
ARE SNNS TRULY ENERGY-EFFICIENT? - A HARDWARE PERSPECTIVE
Abhiroop Bhattacharjee, Ruokai Yin, Abhishek Moitra, Priyadarshini Panda, Yale University, United States of America
Session:
SS-L17: Algorithm-Hardware Co-Design of Neuromorphic Solutions for Signal Processing Applications Lecture
Track:
Special Sessions
Location:
Room E4
Presentation Time:
Fri, 19 Apr, 09:00 - 09:20 (UTC +9)
Session Co-Chairs:
Peter A. Beerel, University of Southern California and Sumit Bam Shrestha, Intel
Session SS-L17
SS-L17.1: Training Ultra-Low-Latency Spiking Neural Networks from Scratch
Gourav Datta, Zeyu Liu, Peter Beerel, University of Southern California, United States of America
SS-L17.2: RECENT ADVANCES IN SCALABLE ENERGY-EFFICIENT AND TRUSTWORTHY SPIKING NEURAL NETWORKS: FROM ALGORITHMS TO TECHNOLOGY
Souvik Kundu, Intel Labs, United States of America; Rui-Jie Zhu, University of California, Santa Cruz, United States of America; Akhilesh Jaiswal, University of Wisconsin, United States of America; Peter A. Beerel, University of Southern California, United States of America
SS-L17.3: ARE SNNS TRULY ENERGY-EFFICIENT? - A HARDWARE PERSPECTIVE
Abhiroop Bhattacharjee, Ruokai Yin, Abhishek Moitra, Priyadarshini Panda, Yale University, United States of America
SS-L17.4: Hardware-Algorithm Co-design Enabling Processing-in-Pixel-in-Memory (P^2M) for Neuromorphic Vision Sensors
Md Abdullah-Al Kaiser, University of Southern California, United States of America; Akhilesh Jaiswal, University of Wisconsin-Madison, United States of America
SS-L17.5: MEMORY EFFICIENT CORNER DETECTION FOR EVENT-DRIVEN DYNAMIC VISION SENSORS
Pao-Sheng Vincent Sun, City University of Hong Kong, Hong Kong; Arren Glover, Chiara Bartolozzi, Italian Institute of Technology, Italy; Arindam Basu, City University of Hong Kong, Hong Kong
SS-L17.6: EFFICIENT VIDEO AND AUDIO PROCESSING WITH LOIHI 2
Sumit Bam Shrestha, Jonathan Timcheck, Paxon Frady, Leobardo Campos-Macias, Mike Davies, Intel Corporation, United States of America
Contacts