General-purpose lossless data compression and decompression ("(de)compression") are used widely in hyperscale systems and are key "datacenter taxes". However, designing optimal hardware compression and decompression processing units ("CDPUs") is challenging due to the variety of algorithms deployed, input data characteristics, and evolving costs of CPU cycles, network bandwidth, and memory/storage capacities.

The timing of individual neuronal spikes is essential for biological brains to make fast responses to sensory stimuli. However, conventional artificial neural networks lack the intrinsic temporal coding ability present in biological networks. We propose a spiking neural network model that encodes information in the relative timing of individual spikes. In classification tasks, the output of the network is indicated by the first neuron to spike in the output layer. This temporal coding scheme allows the supervised training of the network with backpropagation, using locally exact derivatives of the postsynaptic spike times with respect to presynaptic spike times. The network operates using a biologically plausible synaptic transfer function. In addition, we use trainable pulses that provide bias, add flexibility during training, and exploit the decayed part of the synaptic function. We show that such networks can be successfully trained on multiple data sets encoded in time, including MNIST. Our model outperforms comparable spiking models on MNIST and achieves similar quality to fully connected conventional networks with the same architecture. The spiking network spontaneously discovers two operating modes, mirroring the accuracy-speed tradeoff observed in human decision-making: a highly accurate but slow regime, and a fast but slightly lower accuracy regime. These results demonstrate the computational power of spiking networks with biological characteristics that encode information in the timing of individual neurons. By studying temporal coding in spiking networks, we aim to create building blocks toward energy-efficient, state-based biologically inspired neural architectures. We provide open-source code for the model.

We propose a spiking neural network model that encodes information in the relative timing of individual neuron spikes and performs classification using the first output neuron to spike. This temporal coding scheme allows the supervised training of the network with backpropagation, using locally exact derivatives of the postsynaptic with respect to presynaptic spike times. The network uses a biologically-inspired alpha synaptic transfer function and trainable synchronisation pulses as temporal references. We successfully train the network on the MNIST dataset encoded in time. Our spiking neural network outperforms comparable spiking models and achieves similar accuracy to a fully connected conventional network. During training, our network displays a speed-accuracy trade-off, with either slow and highly-accurate or very fast but less accurate classification. The results demonstrate the computational power of spiking networks with biological characteristics that encode information in the timing of individual neurons. Our code is publicly available.

Brotli is an open source general-purpose data compressor introduced by Google in late 2013 and now adopted in most known browsers and Web servers. It is publicly available on GitHub and its data format was submitted as RFC 7932 in July 2016. Brotli is based on the Lempel-Ziv compression scheme and planned as a generic replacement of Gzip and ZLib. The main goal in its design was to compress data on the Internet, which meant optimizing the resources used at decoding time, while achieving maximal compression density. This article is intended to provide the first thorough, systematic description of the Brotli format as well as a detailed computational and experimental analysis of the main algorithmic blocks underlying the current encoder implementation, together with a comparison against compressors of different families constituting the state-of-the-art either in practice or in theory. This treatment will allow us to raise a set of new algorithmic and software engineering problems that deserve further attention from the scientific community.

Gipfeli is a high-speed compression algorithm that uses backward references with a 16-bit sliding window, based on 1977 paper by Lempel and Ziv, enriched with an ad-hoc entropy coding for both literals and backward references. We have implemented it in C++ and fine-tuned for very high performance. The compression ratio is similar to Zlib in the fastest mode, but Gipfeli is more than three times faster. This positions it as an ideal solution for many bandwidth-bound systems, intermediate data storage and parallel computations.