Golomb – Rice Coding for Efficient Compression of 8-Bit Floating-Point Data

Recent advancements in neural network (NN) hardware and software have introduced 8-bit Floating-Point (FP8) format as promising candidate for reducing memory usage and computational overhead. This article investigates the potential for further compressing FP8) data by applying Golomb–Rice (GR) coding. In the FP8 format, the 8 bits are allocated to represent the sign, exponent and mantissa using natural binary encoding. To achieve efficient compression, the GR code, a variable-length, lossless coding scheme, is applied exclusively to the exponent bits. In this way, data storage savings could be achieved, which can be very significant when the large amount of data has to be stored, such as storing NNs.