arxivst stuff from arxiv that you should probably bookmark

ApproxDBN: Approximate Computing for Discriminative Deep Belief Networks

Abstract · Apr 13, 2017 05:04 ·

limited belief power approxdbn approximate implementations precision methodology resilient discriminative cs-ne

Arxiv Abstract

  • Xiaojing Xu
  • Srinjoy Das
  • Ken Kreutz-Delgado

Probabilistic generative neural networks are useful for many applications, such as image classification, speech recognition and occlusion removal. However, the power budget for hardware implementations of neural networks can be extremely tight. To address this challenge we describe a design methodology for using approximate computing methods to implement Approximate Deep Belief Networks (ApproxDBNs) by systematically exploring the use of (1) limited precision of variables; (2) criticality analysis to identify the nodes in the network which can operate with such limited precision while allowing the network to maintain target accuracy levels; and (3) a greedy search methodology with incremental retraining to determine the optimal reduction in precision to enable maximize power savings under user-specified accuracy constraints. Experimental results show that significant bit-length reduction can be achieved by our ApproxDBN with constrained accuracy loss.

Read the paper (pdf) »