Fledge: Flexible Edge Platforms Enabled by In-memory Computing
Kamalika Datta1,a, Arko Dutt1,b, Ahmed Zaky1,c, Umesh Chand2,e, Devendra Singh2,f, Yida Li2,g, Jackson Chun-Yang Huang2,h, Aaron Thean2,i and Mohamed M Sabry Aly1,d
1 Nanyang Technological University Singapore
akamalika.datta@ntu.edu.sg
barko001@e.ntu.edu.sg
cahmed.zaky@ntu.edu.sg
dmsabry@ntu.edu.sg
2 National University of Singapore
eumesh.chand@nus.edu.sg
fdevendra.singh@nus.edu.sg
gli.yida@nus.edu.sg
hjackson.huang@nus.edu.sg
iaaron.thean@nus.edu.sg
ABSTRACT
The proliferation of advanced analytics and artificial intelligence has been driven by huge volumes of data that are mostly generated at the edge. Simultaneously, there is a rising demand to perform analytics on edge platforms (i.e., near-sensor data analytics). However, conventional architectures of such platforms may not execute the targeted applications in an energy-efficient manner. Emerging near and in-memory computing paradigms can increase the energy efficiency of edge platforms by relying on emerging logic and memory devices. More importantly, these paradigms enable the possibility of performing computations on unconventional platforms, namely flexible computing systems. In this paper, we explore the benefits of in-memory computing at the edge on a flexible substrate enabled by thin-film transistors (TFTs) and resistive RAM (RRAM). As a case study, we consider bio-signal processing application workloads, i.e., compressive sensing and anomaly detection. We model the device, circuit, and architecture of our targeted platform and evaluate the corresponding systemlevel performance. Preliminary results indicate that in-memory computing enabled by flexible electronic devices enables a new class of edge platforms with lower power consumption, compared to that of rigid TFT devices.
