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Found 4 papers, 1 papers with code
Efficient non-uniform quantizer for quantized neural network targeting reconfigurable hardware
While most works use uniform quantizers for both parameters and activations, it is not always the optimal one, and a non-uniform quantizer need to be considered.
NICE: Noise Injection and Clamping Estimation for Neural Network Quantization
Convolutional Neural Networks (CNN) are very popular in many fields including computer vision, speech recognition, natural language processing, to name a few.
UNIQ: Uniform Noise Injection for Non-Uniform Quantization of Neural Networks
We present a novel method for neural network quantization that emulates a non-uniform $k$-quantile quantizer, which adapts to the distribution of the quantized parameters.
Streaming Architecture for Large-Scale Quantized Neural Networks on an FPGA-Based Dataflow Platform
Using quantized values enables the use of FPGAs to run NNs, since FPGAs are well fitted to these primitives; e. g., FPGAs provide efficient support for bitwise operations and can work with arbitrary-precision representation of numbers.
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