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We present PrecisionBatching, a quantized inference algorithm for speeding upneural network execution on traditional hardware platforms at low bitwidthswithout the need for retraining or recalibration. PrecisionBatching decomposesa neural network into individual bitlayers and accumulates them using fast1-bit operations while maintaining activations in full precision.PrecisionBatching not only facilitates quantized inference at low bitwidths (<8 bits) without the need for retraining/recalibration, but also 1) enablestraditional hardware platforms the ability to realize inference speedups at afiner granularity of quantization (e.g: 1-16 bit execution) and 2) allowsaccuracy and speedup tradeoffs at runtime by exposing the number of bitlayersto accumulate as a tunable parameter. Across a variety of applications (MNIST,language modeling, natural language inference) and neural network architectures(fully connected, RNN, LSTM), PrecisionBatching yields end-to-end speedups ofover 8x on a GPU within a < 1% error margin of the full precision baseline,outperforming traditional 8-bit quantized inference by over 1.5x-2x at the sameerror tolerance.

Quantized Neural Network Inference with Precision Batching