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Fine Grain Precision Scaling for Datapath Approximations in Digital Signal Processing Systems

Abstract : 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.
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Seogoo Lee, Andreas Gerstlauer. Fine Grain Precision Scaling for Datapath Approximations in Digital Signal Processing Systems. 21th IFIP/IEEE International Conference on Very Large Scale Integration - System on a Chip (VLSI-SoC), Oct 2013, Istanbul, Turkey. pp.119-143, ⟨10.1007/978-3-319-23799-2_6⟩. ⟨hal-01380301⟩

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