Run-Time Accuracy Reconfigurable Stochastic Computing for Dynamic Reliability and Power Management
Shuyuan Yu, Han Zhou, Shaoyi Peng, Hussam Amrouch, Joerg Henkel,, Sheldon X.-D. Tan

TL;DR
This paper introduces a novel run-time reconfigurable stochastic computing framework that dynamically adjusts accuracy to manage aging effects, power, and reliability, demonstrated on image compression hardware with significant power savings.
Contribution
The paper presents a new accuracy-reconfigurable stochastic computing framework enabling run-time accuracy adjustments for aging and power management, unlike traditional static approaches.
Findings
Mitigates aging effects via accuracy trade-offs with frequency scaling.
Achieves 74% power savings with minimal image quality loss.
Maintains throughput while compensating for long-term aging effects.
Abstract
In this paper, we propose a novel accuracy-reconfigurable stochastic computing (ARSC) framework for dynamic reliability and power management. Different than the existing stochastic computing works, where the accuracy versus power/energy trade-off is carried out in the design time, the new ARSC design can change accuracy or bit-width of the data in the run-time so that it can accommodate the long-term aging effects by slowing the system clock frequency at the cost of accuracy while maintaining the throughput of the computing. We validate the ARSC concept on a discrete cosine transformation (DCT) and inverse DCT designs for image compressing/decompressing applications, which are implemented on Xilinx Spartan-6 family XC6SLX45 platform. Experimental results shows that the new design can easily mitigate the long-term aging induced effects by accuracy trade-off while maintaining the…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Error Correcting Code Techniques · CCD and CMOS Imaging Sensors
