VLSI Implementation of Deep Neural Network Using Integral Stochastic Computing

The hardware implementation of deep neural networks (DNNs) has recently received tremendous attention: many applications in fact require high-speed operations that suit a hardware implementation. However, numerous elements and complex interconnections are usually required, leading to a large area occupation and copious power consumption. Stochastic computing has shown promising results for low-power area-efficient hardware implementations, even though existing stochastic algorithms require long streams that cause long latencies. In this paper, we propose an integer form of stochastic computation and introduce some elementary circuits. We then propose an efficient implementation of a DNN based on integral stochastic computing. The proposed architecture has been implemented on a Virtex7 FPGA, resulting in 45% and 62% average reductions in area and latency compared to the best reported architecture in literature. We also synthesize the circuits in a 65 nm CMOS technology and we show that the proposed integral stochastic architecture results in up to 21% reduction in energy consumption compared to the binary radix implementation at the same misclassification rate. Due to fault-tolerant nature of stochastic architectures, we also consider a quasi-synchronous implementation which yields 33% reduction in energy consumption w.r.t. the binary radix implementation without any compromise on performance.Comment: 11 pages, 12 figure

Relative multiplexing for minimizing switching in linear-optical quantum computing

Many existing schemes for linear-optical quantum computing (LOQC) depend on multiplexing (MUX), which uses dynamic routing to enable near-deterministic gates and sources to be constructed using heralded, probabilistic primitives. MUXing accounts for the overwhelming majority of active switching demands in current LOQC architectures. In this manuscript, we introduce relative multiplexing (RMUX), a general-purpose optimization which can dramatically reduce the active switching requirements for MUX in LOQC, and thereby reduce hardware complexity and energy consumption, as well as relaxing demands on performance for various photonic components. We discuss the application of RMUX to the generation of entangled states from probabilistic single-photon sources, and argue that an order of magnitude improvement in the rate of generation of Bell states can be achieved. In addition, we apply RMUX to the proposal for percolation of a 3D cluster state in [PRL 115, 020502 (2015)], and we find that RMUX allows a 2.4x increase in loss tolerance for this architecture.Comment: Published version, New Journal of Physics, Volume 19, June 201

ハードウェアリソースの高稼働率化に基づく細粒度多値リコンフィギャラブルVLSIアーキテクチャ

Tohoku University亀山充隆課

Hardware-based Security for Virtual Trusted Platform Modules

Virtual Trusted Platform modules (TPMs) were proposed as a software-based alternative to the hardware-based TPMs to allow the use of their cryptographic functionalities in scenarios where multiple TPMs are required in a single platform, such as in virtualized environments. However, virtualizing TPMs, especially virutalizing the Platform Configuration Registers (PCRs), strikes against one of the core principles of Trusted Computing, namely the need for a hardware-based root of trust. In this paper we show how strength of hardware-based security can be gained in virtual PCRs by binding them to their corresponding hardware PCRs. We propose two approaches for such a binding. For this purpose, the first variant uses binary hash trees, whereas the other variant uses incremental hashing. In addition, we present an FPGA-based implementation of both variants and evaluate their performance

Access and metro network convergence for flexible end-to-end network design

This paper reports on the architectural, protocol, physical layer, and integrated testbed demonstrations carried out by the DISCUS FP7 consortium in the area of access - metro network convergence. Our architecture modeling results show the vast potential for cost and power savings that node consolidation can bring. The architecture, however, also recognizes the limits of long-reach transmission for low-latency 5G services and proposes ways to address such shortcomings in future projects. The testbed results, which have been conducted end-to-end, across access - metro and core, and have targeted all the layers of the network from the application down to the physical layer, show the practical feasibility of the concepts proposed in the project