Kolloquien
Wintersemester 2021/2022
URL zum ICS-Kalender dieses Seminars
Kirchhoff-Institut für Physik, Otto-Haxel-Hörsaal
freitags 17:15
Vorträge
11.2.2022 17:00
INF 308, Hörsaal 1
Kolloquium Wolfram Pernice verschoben auf das SS2022
Ever noticed that annoying lag that sometimes happens during the internet streaming
from, say, your favorite football game? Called latency, this brief delay between a
camera capturing an event and the event being shown to viewers is surely annoying
during the decisive goal at a World Cup final. But it could be deadly for a passenger of
a self-driving car that detects an object on the road ahead and sends images to the
cloud for processing. A way to dramatically reduce latency in artificial intelligence (AI)
systems lies in using light for computation instead of electronic circuits. Combining
photonic processing with what’s known as the non-von Neumann, in-memory
computing paradigm enables to perform computations with unprecedented, ultra-low
latency and compute density. Photonic tensor cores run computations at a processing
speed higher than ever before and perform key computational primitives associated
with AI models such as deep neural networks for computer vision, with remarkable
areal and energy efficiency. While scientists first started tinkering with photonic
processors back in the 1950s, in-memory computing (IMC) is an emerging non-von
Neumann compute paradigm where memory devices, organized in a computational
memory unit, are used for both processing and memory. By removing the need to
shuttle data around between memory and processing units, IMC even with
conventional electronic memory devices could bring significant latency gains.
However, the combination of photonics with IMC could further reduce the latency issue
– so efficiently that photonic in-memory computing might soon play a key role in
latency-critical AI applications. Together with i n-memory computing, photonic
processing overcomes the seemingly insurmountable barrier to the bandwidth of
conventional AI computing systems based on electronic processors.