Generating Stochastic Bitstreams

Stochastic computing (SC) hinges on the generation and use of stochastic bitstreams—streams of randomly generated 1s and 0s, with the probabilities of p and 1 − p, respectively. We consider approaches for stochastic bitstream generation, considering randomness, circuit area/performance/cost, and the impact of the various approaches on SC accuracy. We first review the widely used Linear-Feedback Shift Register (LFSR)-based approach and variants. Alternative low-discrepancy sequences are then discussed, followed by techniques that leverage post-CMOS technologies and metastability of devices as sources of randomness. We conclude with a discussion on correlations between bitstreams, and how (1) correlations can be reduced/eliminated, and (2) correlations may actually be leveraged to positive effect in certain circumstances.

[1]  Yi Wu,et al.  Generating multiple correlated probabilities for MUX-based stochastic computing architecture , 2014, 2014 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).

[2]  John P. Hayes,et al.  Isolation-based decorrelation of stochastic circuits , 2016, 2016 IEEE 34th International Conference on Computer Design (ICCD).

[3]  Berk Sunar,et al.  A Provably Secure True Random Number Generator with Built-In Tolerance to Active Attacks , 2007, IEEE Transactions on Computers.

[4]  Kaushik Roy,et al.  Spintastic: Spin-based stochastic logic for energy-efficient computing , 2015, 2015 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[5]  Yiran Chen,et al.  Spintronics based stochastic computing for efficient Bayesian inference system , 2017, 2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC).

[6]  Jason Helge Anderson,et al.  Effect of LFSR seeding, scrambling and feedback polynomial on stochastic computing accuracy , 2016, 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[7]  Róbert Lórencz,et al.  True random number generator based on ring oscillator PUF circuit , 2017, Microprocess. Microsystems.

[8]  Lirida A. B. Naviner,et al.  Stochastic computation with Spin Torque Transfer Magnetic Tunnel Junction , 2015, 2015 IEEE 13th International New Circuits and Systems Conference (NEWCAS).

[9]  I. A. Antonov,et al.  An economic method of computing LPτ-sequences , 1979 .

[10]  I. Sobol.  Uniformly distributed sequences with an additional uniform property , 1976 .

[11]  Harald Niederreiter,et al.  Random number generation and Quasi-Monte Carlo methods , 1992, CBMS-NSF regional conference series in applied mathematics.

[12]  Piotr Z. Wieczorek.  Dual-metastability fpga-based true random number generator , 2013 .

[13]  John P. Hayes,et al.  Fast and accurate computation using stochastic circuits , 2014, 2014 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[14]  Deian Stefan,et al.  Low discrepancy sequences for Monte Carlo simulations on reconfigurable platforms , 2008, 2008 International Conference on Application-Specific Systems, Architectures and Processors.

[15]  John P. Hayes,et al.  Building a Better Random Number Generator for Stochastic Computing , 2017, 2017 Euromicro Conference on Digital System Design (DSD).

[16]  Zhengya Zhang,et al.  A Native Stochastic Computing Architecture Enabled by Memristors , 2014, IEEE Transactions on Nanotechnology.

[17]  Paul Bratley,et al.  Algorithm 659: Implementing Sobol's quasirandom sequence generator , 1988, TOMS.

[18]  David J. Lilja,et al.  Accelerating the performance of stochastic encoding-based computations by sharing bits in consecutive bit streams , 2013, 2013 IEEE 24th International Conference on Application-Specific Systems, Architectures and Processors.

[19]  Tsuyoshi Iwagaki,et al.  Compact and accurate stochastic circuits with shared random number sources , 2014, 2014 IEEE 32nd International Conference on Computer Design (ICCD).

[20]  Puneet Gupta,et al.  Hybrid VC-MTJ/CMOS non-volatile stochastic logic for efficient computing , 2017, Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017.

[21]  Naoya Onizawa,et al.  Analog-to-stochastic converter using magnetic-tunnel junction devices , 2014, 2014 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH).

[22]  J. Halton.  On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals , 1960 .

[23]  Shuichi Ichikawa,et al.  FPGA Implementation of Metastability-Based True Random Number Generator , 2009, IEICE Trans. Inf. Syst..

[24]  Jie Han,et al.  Energy efficient stochastic computing with Sobol sequences , 2017, Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017.

[25]  Xin Li,et al.  An Architecture for Fault-Tolerant Computation with Stochastic Logic , 2011, IEEE Transactions on Computers.

[26]  Ramu Seva,et al.  Energy-Efficient FPGA-Based Parallel Quasi-Stochastic Computing , 2017 .

[27]  Lirida A. B. Naviner,et al.  A novel circuit design of true random number generator using magnetic tunnel junction , 2016, 2016 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH).