Paper Info
Title
A Design Methodology for Post-Moore’s Law Accelerators: The Case of a Photonic Neuromorphic Processor
Abstract
Over the past decade alternative technologies have gained momentum as conventional digital electronics continue to approach their limitations, due to the end of Moore’s Law and Dennard Scaling. At the same time, we are facing new application challenges such as those due to the enormous increase in data. The attention, has therefore, shifted from homogeneous computing to specialized heterogeneous solutions. As an example, brain-inspired computing has re-emerged as a viable solution for many applications. Such new processors, however, have widened the abstraction gamut from device level to applications. Therefore, efficient abstractions that can provide vertical design-flow tools for such technologies became critical. Photonics in general, and neuromorphic photonics in particular, are among the promising alternatives to electronics. While the arsenal of device level toolbox for photonics, and high-level neural network platforms are rapidly expanding, there has not been much work to bridge this gap. Here, we present a design methodology to mitigate this problem by extending high-level hardware-agnostic neural network design tools with functional and performance models of photonic components. In this paper we detail this tool and methodology by using design examples and associated results. We show that adopting this approach enables designers to efficiently navigate the design space and devise hardware-aware systems with alternative technologies.
Year
DOI
Venue
2020
10.1109/ASAP49362.2020.00028
2020 IEEE 31st International Conference on Application-specific Systems, Architectures and Processors (ASAP)
DocType
ISSN
ISBN
Conference
2160-0511
978-1-7281-7279-8
Citations 
PageRank 
References 
0
0.34
4
Authors
3
Name
Order
Citations
PageRank
Armin Mehrabian143.24
J Sorger2789.98
tarek elghazawi369784.30