Title | ||
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Fine Grain Precision Scaling For Datapath Approximations In Digital Signal Processing Systems |
Abstract | ||
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Finding optimal word lengths in digital signal processing systems has been one of the primary mechanisms for reducing complexity. Recently, this topic has been explored in a broader approximate computing context, where architectures allowing for fine-grain control of hardware or software accuracy have been proposed. One of the obstacles for adoption of fine-grain scaling techniques is that they require determining the precision of all intermediate values at all possible operation points, making simulation-based optimization infeasible. In this chapter, we study efficient analytical heuristics to find optimal sets of word lengths for all variables and operations in a dataflow graph constrained by mean squared error type of metrics. We apply our method to several industrial-strength examples. Our results show a more than 5,000x improvement in optimization time compared to an efficient simulation-based word length optimization method with less than 10% estimation error across a range of target quality metrics. |
Year | DOI | Venue |
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2013 | 10.1007/978-3-319-23799-2_6 | VLSI-SOC: AT THE CROSSROADS OF EMERGING TRENDS |
Keywords | DocType | Volume |
Power reduction, Approximate computing, Word length optimization, Digital signal processing | Conference | 461 |
ISSN | Citations | PageRank |
1868-4238 | 2 | 0.40 |
References | Authors | |
9 | 2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Seogoo Lee | 1 | 29 | 2.84 |
Andreas Gerstlauer | 2 | 890 | 78.75 |