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Invocation-driven Neural Approximate Computing with a Multiclass-Classifier and Multiple Approximators
arXiv - CS - Machine Learning Pub Date : 2018-10-19 , DOI: arxiv-1810.08379 Haiyue Song, Chengwen Xu, Qiang Xu, Zhuoran Song, Naifeng Jing, Xiaoyao Liang, Li Jiang
arXiv - CS - Machine Learning Pub Date : 2018-10-19 , DOI: arxiv-1810.08379 Haiyue Song, Chengwen Xu, Qiang Xu, Zhuoran Song, Naifeng Jing, Xiaoyao Liang, Li Jiang
Neural approximate computing gains enormous energy-efficiency at the cost of
tolerable quality-loss. A neural approximator can map the input data to output
while a classifier determines whether the input data are safe to approximate
with quality guarantee. However, existing works cannot maximize the invocation
of the approximator, resulting in limited speedup and energy saving. By
exploring the mapping space of those target functions, in this paper, we
observe a nonuniform distribution of the approximation error incurred by the
same approximator. We thus propose a novel approximate computing architecture
with a Multiclass-Classifier and Multiple Approximators (MCMA). These
approximators have identical network topologies and thus can share the same
hardware resource in a neural processing unit(NPU) clip. In the runtime, MCMA
can swap in the invoked approximator by merely shipping the synapse weights
from the on-chip memory to the buffers near MAC within a cycle. We also propose
efficient co-training methods for such MCMA architecture. Experimental results
show a more substantial invocation of MCMA as well as the gain of
energy-efficiency.
更新日期：2018-10-22
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