YodaNN: An Architecture for Ultra-Low Power Binary-Weight CNN Acceleration

Convolutional neural networks (CNNs) have revolutionized the world of computer vision over the last few years, pushing image classification beyond human accuracy. The computational effort of today's CNNs requires power-hungry parallel processors or GP-GPUs. Recent developments in CNN accelerators for system-on-chip integration have reduced energy consumption significantly. Unfortunately, even these highly optimized devices are above the power envelope imposed by mobile and deeply embedded applications and face hard limitations caused by CNN weight I/O and storage. This prevents the adoption of CNNs in future ultra-low power Internet of Things end-nodes for near-sensor analytics. Recent algorithmic and theoretical advancements enable competitive classification accuracy even when limiting CNNs to binary (+1/-1) weights during training. These new findings bring major optimization opportunities in the arithmetic core by removing the need for expensive multiplications, as well as reducing I/O bandwidth and storage. In this work, we present an accelerator optimized for binary-weight CNNs that achieves 1510 GOp/s at 1.2 V on a core area of only 1.33 MGE (Million Gate Equivalent) or 0.19 mm$^2$ and with a power dissipation of 895 {\mu}W in UMC 65 nm technology at 0.6 V. Our accelerator significantly outperforms the state-of-the-art in terms of energy and area efficiency achieving 61.2 TOp/s/[email protected] V and 1135 GOp/s/[email protected] V, respectively

GeNN: a code generation framework for accelerated brain simulations

Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ. GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials, Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/

Introduction of shared-memory parallelism in a distributed-memory multifrontal solver

We study the adaptation of a parallel distributed-memory solver towards a shared-memory code, targeting multi-core architectures. The advantage of adapting the code over a new design is to fully benefit from its numerical kernels, range of functionalities and internal features. Although the studied code is a direct solver for sparse systems of linear equations, the approaches described in this paper are general and could be useful to a wide range of applications. We show how existing parallel algorithms can be adapted to an OpenMP environment while, at the same time, also relying on third-party optimized multithreaded libraries. We propose simple approaches to take advantage of NUMA architectures, and original optimizations to limit thread synchronization costs. For each point, the performance gains are analyzed in detail on test problems from various application areas.Dans cet article, nous étudions l'adaptation d'un code parallèle à mémoire distribuée en un code visant les architectures à mémoire partagée de type multi-coeurs. L'intérêt d'adapter un code existant plutôt que d'en concevoir un nouveau est de pouvoir bénéficier directement de toute la richesse de ses fonctionnalités numériques ainsi que de ses caractéristiques internes. Même si le code sur lequel porte l'étude est un solveur direct multifrontale pour systèmes linéaires creux, les algorithmes et techniques discutés sont générales et peuvent s'appliquer à des domaines d'application plus généraux. Nous montrons comment des algorithmes parallèles existant peuvent être adaptés à un environnement OpenMP tout en exploitant au mieux des librairies existantes optimisées. Nous présentons des approches simples pour tirer parti des spécificités des architectures NUMA, ainsi que des optimisations originales permettant de limiter les coûts de synchronisation dans le modèle fork-join que l'on utilise. Pour chacun de ces points, les gains en performance sont analysés sur des cas tests provenant de domaines d'applications variés