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Stochastic-computing-based-neural-network-accelerator's Introduction

Stochastic-computing-based-neural-network-accelerator

Stochastic Number Generator (SNG)

2012

  • 2012 ICCAD: An Efficient Implementation of Numerical Integration Using Logical Computation on Stochastic Bit Streams (University of Michigan–Shanghai Jiao Tong University Joint Institute)

2013

  • 2013 ASPDAC: Optimizing Multi-level Combinational Circuits for Generating Random Bits (University of Michigan–Shanghai Jiao Tong University Joint Institute)

2014

  • 2014 DATE: Fast and Accurate Computation Using Stochastic Circuits (University of Michigan, Ann Arbor)

2016

  • 2016 ICCAD: A Deterministic Approach to Stochastic Computation (University of Minnesota)
  • 2016 DATE: Effect of LFSR Seeding, Scrambling and Feedback Polynomial on Stochastic Computing Accuracy (University of Toronto; Tokyo Institute of Technology; Ritsumeikan University)

2017

  • 2017 DATE: Energy Efficient Stochastic Computing with Sobol Sequences (University of Alberta)
  • 2017 DSD: Building a Better Random Number Generator for Stochastic Computing (University of Passau; University of Michigan, Ann Arbor)
  • 2017 ICCAD: Design of Accurate Stochastic Number Generators with Noisy Emerging Devices for Stochastic Computing (University of Michigan–Shanghai Jiao Tong University Joint Institute)
  • 2017 IWLS: Design of Reliable Stochastic Number Generators Using Emerging Devices for Stochastic Computing (University of Michigan–Shanghai Jiao Tong University Joint Institute)

2018

  • 2018 ICCAD: Deterministic Methods for Stochastic Computing Using Low-Discrepancy Sequences (University of Louisiana at Lafayette)
  • 2018 TCAD: An Efficient and Accurate Stochastic Number Generator Using Even-Distribution Coding. (NIST, Samsung, SNU)
  • 2018 Microprocessors and Microsystems: S-Box-Based Random Number Generation for Stochastic Computing (University of Passau; University of Michigan, Ann Arbor)
  • 2018 TVLSI: Toward Energy-Efficient Stochastic Circuits Using Parallel Sobol Sequences (University of Alberta)
  • 2018 ISVLSI: Towards Theoretical Cost Limit of Stochastic Number Generators for Stochastic Computing (University of Michigan–Shanghai Jiao Tong University Joint Institute)
  • 2018 ICRC: SC-SD: Towards Low Power Stochastic Computing Using Sigma Delta Streams. (University of Virginia Charlottesville)

2019

  • 2019 arxiv: Synthesizing Number Generators for Stochastic Computing using Mixed Integer Programming. (Washington)
  • 2019 TED:Spin-Hall-Effect-Based Stochastic Number Generator for Parallel Stochastic Computing. (Minnesota)
  • 2019 SNW:A Parallel Bitstream Generator for Stochastic Computing. (Peking)

Accuracy Analysis

  • 2015 GLSVLSI: Minimizing Error of Stochastic Computation through Linear Transformation (University of Michigan–Shanghai Jiao Tong University Joint Institute)
  • 2018 JETC: Framework for Quantifying and Managing Accuracy in Stochastic Circuit Design (University of Michigan, Ann Arbor)
  • 2018 DATE: Correlation manipulating circuits for stochastic computing. (Washington)
  • 2018 ISOCC: Accurate Stochastic Computing Using a Wire Exchanging Unipolar Multiplier. (Kwangwoon University)
  • 2018 ISOCC: Generalized Adaptive Variable Bit Truncation Method for Approximate Stochastic Computing. (Missouri Univ of Science & Technology, Daegu University, Northeastern)

Neural Networks

2016

  • 2016 DAC: Dynamic Energy-Accuracy Trade-off Using Stochastic Computing in Deep Neural Networks. (Samsung, Seoul National University, Ulsan National Institute of Science and Technology)

2017

  • 2017 ASPLOS: SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing. (Syracuse University, USC, The City College of New York)
  • 2017 DAC: New Stochastic Computing Multiplier and Its Application to Deep Neural Networks. (UNIST)
  • 2017 ICCAD: Deep reinforcement learning: Framework, applications, and embedded implementations: Invited paper. (Syracuse, University of California)
  • 2017 DATE: Structural Design Optimization for Deep Convolutional Neural Networks Using Stochastic Computing. (Syracuse, USC, CUNY)
  • 2017 DATE: Energy-Efficient Hybrid Stochastic-Binary Neural Networks for Near-Sensor Computing. (University of Washington, University of Michigan)
  • 2017 DATE: Magnetic tunnel junction enabled all-spin stochastic spiking neural network. (Purdue)
  • 2017 ICCD: Accurate and Efficient Stochastic Computing Hardware for Convolutional Neural Networks. (Syracuse, USC, CUNY)
  • 2017 ICCD: Neural Network Classifiers Using Stochastic Computing with a Hardware-Oriented Approximate Activation Function. (UMN, CUNY)
  • 2017 TVLSI: VLSI Implementation of Deep Neural Network Using Integral Stochastic Computing. (McGill University, Tohoku University)
  • 2017 ASP-DAC: Scalable Stochastic-computing Accelerator for Convolutional Neural Networks. (UNIST, Seoul National University)
  • 2017 ASP-DAC: Towards Acceleration of Deep Convolutional Neural Networks Using Stochastic Computing. (USC, Syracuse, CUNY)
  • 2017 ISLPED: Power optimizations in MTJ-based Neural Networks through Stochastic Computing. (University of Maryland)
  • 2017 ISQED: Stochastic-based multi-stage streaming realization of deep convolutional neural network. (University of Central Florida)
  • 2017 IJCNN: Hardware-driven nonlinear activation for stochastic computing based deep convolutional neural networks (USC, Syracuse)
  • 2017 GLSVLSI: Softmax Regression Design for Stochastic Computing Based Deep Convolutional Neural Networks. (USC, Syracuse, CNNY)
  • 2017 WCSP: Efficient fast convolution architecture based on stochastic computing. (LEADS, City University of New York, Southeast)
  • 2017 VLSI-DAT: Hybrid spiking-stochastic Deep Neural Network. (Seoul National University)
  • 2017 Big Data: Energy efficient stochastic-based deep spiking neural networks for sparse datasets. (Oak Ridge National Laboratory)
  • 2017 TCAS-II: Fully-Parallel Area-Efficient Deep Neural Network Design Using Stochastic Computing. (City University of New York, Syracuse, Nanjing University)
  • 2017 Integration, the VLSI Journal: Normalization and dropout for stochastic computing-based deep convolutional neural networks. (University of Southern California, Syracuse, City University of New York)
  • 2017 International Journal of Approximate Reasoning: Quick and energy-efficient Bayesian computing of binocular disparity using stochastic digital signals. (ISIR)

2018

  • 2018 DAC: Sign-Magnitude SC: Getting 10X Accuracy for Free in Stochastic Computing for Deep Neural Networks. (UNIST)
  • 2018 DAC: DPS: Dynamic Precision Scaling for Stochastic Computing-Based Deep Neural Networks. (UNIST)
  • 2018 DATE: An Energy-efficient Stochastic Computational Deep Belief Network. (Alberta, Syracuse, NEU)
  • 2018 ASP-DAC: Spintronics based stochastic computing for efficient Bayesian inference system. (Beihang, Duke)
  • 2018 FPGA: Routing Magic: Performing Computations Using Routing Networks and Voting Logic on Unary Encoded Data. (Minnesota)

  • 2018 TCAD: HEIF: Highly Efficient Stochastic Computing based Inference Framework for Deep Neural Networks. (Syracuse University, USC, City University of New York)
  • 2018 TCAD: Architecture Considerations for Stochastic Computing Accelerators. (Washington)
  • 2018 TCAD: Gradient Descent Using Stochastic Circuits for Efficient Training of Learning Machines. (University of Alberta, Tsinghua)
  • 2018 ISVLSI: Towards Budget-Driven Hardware Optimization for Deep Convolutional Neural Networks Using Stochastic Computing. (Syracuse, University of Southern California, City University of New York)
  • 2018 ISQED: Quantized Neural Networks with New Stochastic Multipliers. (Minnesota, City University of New York)
  • 2018 ISQED: An area and energy efficient design of domain-wall memory-based deep convolutional neural networks using stochastic computing. (Syracuse, Alberta)
  • 2018 ISQED: Parallel implementation of finite state machines for reducing the latency of stochastic computing. (Minnesota)
  • 2018 GLSVLSI: Design Space Exploration of Magnetic Tunnel Junction based Stochastic Computing in Deep Learning. (Beihang)
  • 2018 GLSVLSI: Bit-Wise Iterative Decoding of Polar Codes using Stochastic Computing. (McGill University)
  • 2018 JETC: An FPGA Implementation of a Time Delay Reservoir Using Stochastic Logic. (Air Force Research Laboratory, Rochester Institute of Technology)
  • 2018 TETC: High Quality Down-Sampling for Deterministic Approaches to Stochastic Computing. (Minnesota)
  • 2018 Computer Architecture Letters: On Memory System Design for Stochastic Computing. (Minnesota)
  • 2018 Transactions on Computers: A Stochastic Computational Multi-Layer Perceptron with Backward Propagation. (Alberta, Syracuse, Northeastern)
  • 2018 DSC: Stochastic Processors on FPGAs to Compute Sensor Data Towards Fault-Tolerant IoT Systems. (INESC-ID)
  • 2018 JESTCS: An Energy-Efficient Online-Learning Stochastic Computational Deep Belief Network. (Alberta, Syracuse)
  • 2018 ACSSC: Area-efficient K-Nearest Neighbor Design using Stochastic Computing. (Rutgers University)
  • 2018 DSP: Low-Complexity Winograd Convolution Architecture Based on Stochastic Computing. (LEADS, Southeast)
  • 2018 APCCAS: Low Cost LSTM Implementation based on Stochastic Computing for Channel State Information Prediction. (University of Electronic Science and Technology of China)
  • 2018 MCSoC: An Efficient Hardware Implementation of Activation Functions Using Stochastic Computing for Deep Neural Networks. (Le Quy Don Technical University)
  • 2018 Journal of Low Power Electronics: Optimization of Softmax Layer in Deep Neural Network Using Integral Stochastic Computation. ( Tsinghua)
  • 2018 NICS: An Efficient Hardware Implementation of Artificial Neural Network based on Stochastic Computing. (SISLAB)
  • 2018 Transactions on Multi-Scale Computing Systems: Scalable FPGA Accelerator for Deep Convolutional Neural Networks with Stochastic Streaming. (Oak Ridge National Laboratory, University of Central Florida)
  • 2018 Neurocomputing: Stochastic learning in deep neural networks based on nanoscale PCMO device characteristics. (New Jersey Institute of Technology, Indian Institute of Technology)
  • 2018 European Physical Journal Plus: A new stochastic computing paradigm for the dynamics of nonlinear singular heat conduction model of the human head. (Institute of Information Technology, HITEC University, Cankaya University)
  • 2018 IEICE: Application of stochastic computing in brainware. (McGill University, Tohoku University)
  • 2018 CSIT: Feature Selection Based on Parallel Stochastic Computing. (Zaporizhzhia National Technical University)

2019

  • 2019 DAC: SkippyNN: An Embedded Stochastic-computing Accelerator forConvolutional Neural Networks. (Univ. of Tehran, Minnesota)
  • 2019 DAC: LAcc: Exploiting Lookup Table-based Fast and Accurate Vector Multiplication in DRAM-based CNN Accelerator. (National Univ. of Defense Technology, Pittsburgh)
  • 2019 DAC: Successive Log Quantization for Cost-Efficient Neural Networks Using Stochastic Computing. (UNIST)
  • 2019 DAC: In-Stream Stochastic Division and Square Root via Correlation. (Wisconsin-Madison)
  • 2019 DATE: Energy-Efficient Convolutional Neural Networks with Deterministic Bit-Stream Processing. (University of Minnesota, University of Louisiana)
  • 2019 ASP-DAC: Log-quantized stochastic computing for memory and computation efficient DNNs. (UNIST)
  • 2019 ASP-DAC: Hybrid binary-unary hardware accelerator. (Minnesota)
  • 2019 TCAS-I: Efficient CMOS Invertible Logic Using Stochastic Computing. (McGill University, Tohoku University)
  • 2019 TCAS-II: New Divider Design for Stochastic Computing.
  • 2019 TCAS-II: A stochastic computing architecture for iterative estimation. (Johannes Kepler Universit)
  • 2019 TCAS-II: High-Accuracy and Fault Tolerant Stochastic Inner Product Design. ()
  • 2019 TCAD: SPINBIS: Spintronics based Bayesian Inference System with Stochastic Computing. (Beihang, University of South California, Duke)
  • 2019 JETC: Low-Cost Stochastic Hybrid Multiplier for Quantized Neural Networks. (Minnesota, University of Louisiana at Lafayette)
  • 2019 JETC: Neural Network Classifiers Using a Hardware-Based Approximate Activation Function with a Hybrid Stochastic Multiplier. (Minnesota, Rutgers University)
  • 2019 VLSI System: Design of FSM-Based Function With Reduced Number of States in Integral Stochastic Computing. (National Kaohsiung University of Science and Technology)
  • 2019 arXiv: From Stochastic to Bit Stream Computing: Accurate Implementation of Arithmetic Circuits and Applications in Neural Networks. (Istanbul Technical University)
  • 2019 AAAI: Universal Approximation Property and Equivalence of Stochastic Computing-Based Neural Networks and Binary Neural Networks. (Northeastern, Syracuse)
  • 2019 CF: On the maximum function in stochastic computing. (Stuttgart, Michigan)
  • 2019 Access: Stochastic Computing for Hardware Implementation of Binarized Neural Networks. ()
  • 2019 TVLSI: An Energy-Efficient and Noise-Tolerant Recurrent Neural Network Using Stochastic Computing. (Alberta, Tsinghua, Northeastern) SC+RNN
  • 2019 ISCAS: Stochastic Computing for Low-Power and High-Speed Deep Learning on FPGA. (James Cook University) SC online training accelerator
  • 2019 ASAP: Efficient Architectures and Implementation of Arithmetic Functions Approximation Based Stochastic Computing. (University College Cork)
  • 2019 ASAP: Context-Aware Number Generator for Deterministic Bit-stream Computing. (Louisiana at Lafayette)
  • 2019 ASAP: Energy-Efficient Near-Sensor Convolution using Pulsed Unary Processing. (Louisiana at Lafayette, Minnesota)
  • 2019 ASAP: Using Residue Number Systems to Accelerate Deterministic Bit-stream Multiplication. (University of Tehran, Louisiana at Lafayette, IPM, Shahid Beheshti University, IROST)
  • 2019 GLSVLSI: An Efficient Time-based Stochastic Computing Circuitry Employing Neuron-MOS. (NAIST)
  • 2019 GLSVLSI: Low Cost Hybrid Spin-CMOS Compressor for Stochastic Neural Networks. (Minnesota)
  • 2019 ISQED: Accelerating Deterministic Bit-Stream Computing with Resolution Splitting. (University of Louisiana, University of Minnesota)
  • 2019 ISQED: Deterministic Stochastic Computation Using Parallel Datapaths. (Texas at Austin)
  • 2019 VLSID: Reducing the Overhead of Stochastic Number Generators Without Increasing Error. (Ritsumeikan University)
  • 2019 Neural Networks: Cost-effective stochastic MAC circuits for deep neural networks. (UNIST)
  • 2019 LCTES: BitBench: a benchmark for bitstream computing. (Wisconsin-Madison)
  • 2019 International Journal of Approximate Reasoning: Bayesian inference using stochastic logic: A study of buffering schemes for mitigating autocorrelation. (Loyola University Maryland)
  • 2019 Neuroscience: ReStoCNet: Residual Stochastic Binary Convolutional Spiking Neural Network for Memory-Efficient Neuromorphic Computing. (Purdue)
  • 2019 ICASSP: Stochastic Data-driven Hardware Resilience to Efficiently Train Inference Models for Stochastic Hardware Implementations. (Princeton) MRAM-PIM
  • 2019 arxiv: Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks. (Luleå University of Technology, Berkeley)
  • 2019: A New Hardware Accelerator for Data Sorting in Area & Energy Constrained Architectures. (Iran University of Science & Technology)
  • 2019 Electronics: Novel Stochastic Computing for Energy-Efficient Image Processors. (Kwangwoon University, Hongik University)

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