BEGIN:VCALENDAR
VERSION:2.0
PRODID:icalendar-ruby
CALSCALE:GREGORIAN
X-WR-CALNAME:On-device Intelligence with Spiking Neural Networks
X-WR-TIMEZONE:Eastern Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20260812T083841Z
UID:tag:localist.com\,2008:EventInstance_45701585299459
DTSTART:20240321T150000Z
DTEND:20240321T160000Z
DESCRIPTION:Spiking Neural Networks (SNNs) have emerged as an alternative t
 o deep learning especially for edge computing due to their huge energy eff
 iciency benefits on neuromorphic hardware. In this presentation\, I will d
 iscuss the roadmap of current activities in the SNN algorithm and hardware
  design space. Particularly\, I will describe our group’s recent works t
 owards enabling and democratizing spike-based machine intelligence design\
 , simulation\, and evaluation across different applications. I will talk a
 bout the importance of temporal dimension in SNNs which unlock unique beha
 vior such as\, robustness and bring in huge benefits in terms of latency\,
  energy\, and accuracy in different applications like video segmentation\,
  human activity recognition\, event sensing among others. Then\, I will de
 lve into the hardware perspective of SNNs and the prospects around memory 
 and sparsity management for accelerating SNNs on general purpose platforms
 . I will highlight some techniques such as\, input-aware dynamic temporal 
 exit and membrane-potential sharing across time. Finally\, I will discuss 
 a future landscape for hardware-software co-design for spike-based on-devi
 ce intelligence.\n\nLocation: SEC 1.307 and Zoom (Password: 599436)
GEO:42.363197;-71.127278
LOCATION:Science and Engineering Complex (SEC)\, SEC 1.307
SUMMARY:On-device Intelligence with Spiking Neural Networks
URL;VALUE=URI:https://events.seas.harvard.edu/event/on-device_intelligence_
 with_spiking_neural_networks
CATEGORIES:Colloquia / Seminar / Lecture
END:VEVENT
END:VCALENDAR