EIE: Efficient Inference Engine on Compressed Deep Neural Network

State-of-the-art deep neural networks (DNNs) have hundreds of millions of connections and are both computationally and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources and power budgets. While custom hardware helps the computation, fetching weights from DRAM is two orders of magnitude more expensive than ALU operations, and dominates the required power. Previously proposed 'Deep Compression' makes it possible to fit large DNNs (AlexNet and VGGNet) fully in on-chip SRAM. This compression is achieved by pruning the redundant connections and having multiple connections share the same weight. We propose an energy efficient inference engine (EIE) that performs inference on this compressed network model and accelerates the resulting sparse matrix-vector multiplication with weight sharing. Going from DRAM to SRAM gives EIE 120x energy saving; Exploiting sparsity saves 10x; Weight sharing gives 8x; Skipping zero activations from ReLU saves another 3x. Evaluated on nine DNN benchmarks, EIE is 189x and 13x faster when compared to CPU and GPU implementations of the same DNN without compression. EIE has a processing power of 102GOPS/s working directly on a compressed network, corresponding to 3TOPS/s on an uncompressed network, and processes FC layers of AlexNet at 1.88x10^4 frames/sec with a power dissipation of only 600mW. It is 24,000x and 3,400x more energy efficient than a CPU and GPU respectively. Compared with DaDianNao, EIE has 2.9x, 19x and 3x better throughput, energy efficiency and area efficiency.Comment: External Links: TheNextPlatform: http://goo.gl/f7qX0L ; O'Reilly: https://goo.gl/Id1HNT ; Hacker News: https://goo.gl/KM72SV ; Embedded-vision: http://goo.gl/joQNg8 ; Talk at NVIDIA GTC'16: http://goo.gl/6wJYvn ; Talk at Embedded Vision Summit: https://goo.gl/7abFNe ; Talk at Stanford University: https://goo.gl/6lwuer. Published as a conference paper in ISCA 201

Design of exponential state estimators for neural networks with mixed time delays

This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Elsevier Ltd.In this Letter, the state estimation problem is dealt with for a class of recurrent neural networks (RNNs) with mixed discrete and distributed delays. The activation functions are assumed to be neither monotonic, nor differentiable, nor bounded. We aim at designing a state estimator to estimate the neuron states, through available output measurements, such that the dynamics of the estimation error is globally exponentially stable in the presence of mixed time delays. By using the Laypunov–Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions to guarantee the existence of the state estimators. We show that both the existence conditions and the explicit expression of the desired estimator can be characterized in terms of the solution to an LMI. A simulation example is exploited to show the usefulness of the derived LMI-based stability conditions.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, the Alexander von Humboldt Foundation of Germany, the Natural Science Foundation of Jiangsu Education Committee of China under Grants 05KJB110154 and BK2006064, and the National Natural Science Foundation of China under Grants 10471119 and 10671172

Influence of Dielectric Environment upon Isotope Effects onGlycoside Heterolysis: Computational Evaluation and AtomicHessian Analysis

Isotope effects depend upon the polarity of the bulk medium in which a chemical process occurs. Implicit solvent calculations with molecule-shaped cavities show that the equilibrium isotope effect (EIE) for heterolysis of the glycosidic bonds in 5′-methylthioadenosine and in 2-(p-nitrophenoxy)tetrahydropyran, both in water, are very sensitive in the range 2 ≤ ε ≤ 10 to the relative permittivity of the continuum surrounding the oxacarbenium ion. However, different implementations of nominally the same PCM method can lead to opposite trends being predicted for the same molecule. Computational modeling of the influence of the inhomogeneous effective dielectric surrounding a substrate within the protein environment of an enzymic reaction requires an explicit treatment. The EIE (KH/KD) for transfer of cyclopentyl, cyclohexyl, tetrahydrofuranyl and tetrahydropyranyl cations from water to cyclohexane is predicted by B3LYP/6-31+G(d) calculations with implicit solvation and confirmed by B3LYP/6-31+G(d)/OPLS-AA calculations with averaging over many explicit solvation configurations. Atomic Hessian analysis, whereby the full Hessian is reduced to the elements belonging to a single atom at the site of isotopic substitution, reveals a remarkable result for both implicit and explicit solvation: the influence of the solvent environment on these EIEs is essentially captured completely by only a 3 × 3 block of the Hessian, although these values must correctly reflect the influence of the whole environment. QM/MM simulation with ensemble averaging has an important role to play in assisting the meaningful interpretation of observed isotope effects for chemical reactions both in solution and catalyzed by enzymes