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Pattern based runtime voltage emergency prediction: An instruction-aware block sparse compressed sensing approach
Conference paper

Pattern based runtime voltage emergency prediction: An instruction-aware block sparse compressed sensing approach

Yu-Guang Chen, Michihiro Shintani, Takashi Sato, Yiyu Shi and Shih-Chieh Chang
Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC, pp.543-548
02/2017

Abstract

Electrical and Electronic Engineering Computer Science Applications Computer Graphics and Computer-Aided Design
The relentless technology scaling calls for reduced supply voltage for dynamic power suppression. On the other hand, transistor threshold voltage cannot be scaled at the same pace to avoid excessive leakage power. Consequently, the noise margin is significantly reduced, leading to the deployment of various noise management systems that handle runtime voltage emergencies. Most of these systems rely on on-chip noise sensors, which are large in size and consume significant power. To tackle this issue, in this paper we propose a sensor-less voltage emergency estimation framework. It explores the relationship between switching activities and noise, and takes advantage of block sparse compressed sensing developed by the signal processing society. Experimental results on a few industrial designs show that by monitoring registers, voltage emergencies can be successfully predicted.

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