NDLI: Designing energy efficient approximate multipliers for neural acceleration

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Designing energy efficient approximate multipliers for neural acceleration

Content Provider	Semantic Scholar
Author	Konofaos, Nikos Novotný, Martin Skavhaug, Amund
Copyright Year	2019
Abstract	Many error resilient applications can be approximated using multi-layer perceptrons (MLPs) with insignificant degradation in output quality. Faster and energy efficient execution of such an application is achieved using a neural accelerator (NA). This work exploits the error resilience characteristics of a MLP by approximating the accelerator itself. An error resilience analysis of the MLP is performed to obtain key constraints which are used for designing energy efficient approximate multipliers. A systematic methodology for the design of approximate multipliers is used. A graph based netlist modification approach is considered. Approximate versions of basic standard cells are generated and these are used to replace accurate cells in the synthesized netlist in a systematic quality controlled manner. These approximate multipliers are further used for approximating the multiply and accumulate (MAC) units in the neural accelerator (NA). The results are validated by considering approximate neural replication of a robotic application, inversek2j. System level energy savings of upto 14% is obtained for less than 7% degradation in output quality. Average application speedup of 24% is obtained over accurate neural accelerator (NA). The results are compared with state-of-the-art approximate multipliers and a comparison with truncation (bit-wise scaling) is performed. Moreover, error healing capability of MLPs is shown by studying the impact of retraining on networks with approximate multipliers. Keywords− Approximate Computing, Machine Learning, Neural Networks, Low Power Design.
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://pure.tue.nl/ws/portalfiles/portal/127560578/Final_for_pure.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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