English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We present an overview of techniques for quantizing convolutional neuralnetworks for inference with integer weights and activations. Per-channelquantization of weights and per-layer quantization of activations to 8-bits ofprecision post-training produces classification accuracies within 2% offloating point networks for a wide variety of CNN architectures. Model sizescan be reduced by a factor of 4 by quantizing weights to 8-bits, even when8-bit arithmetic is not supported. This can be achieved with simple, posttraining quantization of weights.We benchmark latencies of quantized networkson CPUs and DSPs and observe a speedup of 2x-3x for quantized implementationscompared to floating point on CPUs. Speedups of up to 10x are observed onspecialized processors with fixed point SIMD capabilities, like the QualcommQDSPs with HVX.  Quantization-aware training can provide further improvements, reducing thegap to floating point to 1% at 8-bit precision. Quantization-aware trainingalso allows for reducing the precision of weights to four bits with accuracylosses ranging from 2% to 10%, with higher accuracy drop for smallernetworks.We introduce tools in TensorFlow and TensorFlowLite for quantizingconvolutional networks and review best practices for quantization-awaretraining to obtain high accuracy with quantized weights and activations. Werecommend that per-channel quantization of weights and per-layer quantizationof activations be the preferred quantization scheme for hardware accelerationand kernel optimization. We also propose that future processors and hardwareaccelerators for optimized inference support precisions of 4, 8 and 16 bits.

Quantizing deep convolutional networks for efficient inference: A whitepaper