Stochasticcomputingbasedneuralnetworkaccelerator
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 Multilevel 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 LowDiscrepancy Sequences (University of Louisiana at Lafayette)
 2018 TCAD: An Efficient and Accurate Stochastic Number Generator Using EvenDistribution Coding. (NIST, Samsung, SNU)
 2018 Microprocessors and Microsystems: SBoxBased Random Number Generation for Stochastic Computing (University of Passau; University of Michigan, Ann Arbor)
 2018 TVLSI: Toward EnergyEfficient 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: SCSD: 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:SpinHallEffectBased 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 EnergyAccuracy Tradeoff Using Stochastic Computing in Deep Neural Networks. (Samsung, Seoul National University, Ulsan National Institute of Science and Technology)
2017
 2017 ASPLOS: SCDCNN: HighlyScalable 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: EnergyEfficient Hybrid StochasticBinary Neural Networks for NearSensor Computing. (University of Washington, University of Michigan)
 2017 DATE: Magnetic tunnel junction enabled allspin 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 HardwareOriented Approximate Activation Function. (UMN, CUNY)
 2017 TVLSI: VLSI Implementation of Deep Neural Network Using Integral Stochastic Computing. (McGill University, Tohoku University)
 2017 ASPDAC: Scalable Stochasticcomputing Accelerator for Convolutional Neural Networks. (UNIST, Seoul National University)
 2017 ASPDAC: Towards Acceleration of Deep Convolutional Neural Networks Using Stochastic Computing. (USC, Syracuse, CUNY)
 2017 ISLPED: Power optimizations in MTJbased Neural Networks through Stochastic Computing. (University of Maryland)
 2017 ISQED: Stochasticbased multistage streaming realization of deep convolutional neural network. (University of Central Florida)
 2017 IJCNN: Hardwaredriven 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 VLSIDAT: Hybrid spikingstochastic Deep Neural Network. (Seoul National University)
 2017 Big Data: Energy efficient stochasticbased deep spiking neural networks for sparse datasets. (Oak Ridge National Laboratory)
 2017 TCASII: FullyParallel AreaEfficient 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 computingbased deep convolutional neural networks. (University of Southern California, Syracuse, City University of New York)
 2017 International Journal of Approximate Reasoning: Quick and energyefficient Bayesian computing of binocular disparity using stochastic digital signals. (ISIR)
2018
 2018 DAC: SignMagnitude SC: Getting 10X Accuracy for Free in Stochastic Computing for Deep Neural Networks. (UNIST)
 2018 DAC: DPS: Dynamic Precision Scaling for Stochastic ComputingBased Deep Neural Networks. (UNIST)
 2018 DATE: An Energyefficient Stochastic Computational Deep Belief Network. (Alberta, Syracuse, NEU)
 2018 ASPDAC: 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 BudgetDriven 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 domainwall memorybased 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: BitWise 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 DownSampling 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 MultiLayer Perceptron with Backward Propagation. (Alberta, Syracuse, Northeastern)
 2018 DSC: Stochastic Processors on FPGAs to Compute Sensor Data Towards FaultTolerant IoT Systems. (INESCID)
 2018 JESTCS: An EnergyEfficient OnlineLearning Stochastic Computational Deep Belief Network. (Alberta, Syracuse)
 2018 ACSSC: Areaefficient KNearest Neighbor Design using Stochastic Computing. (Rutgers University)
 2018 DSP: LowComplexity 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 MultiScale 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 Stochasticcomputing Accelerator forConvolutional Neural Networks. (Univ. of Tehran, Minnesota)
 2019 DAC: LAcc: Exploiting Lookup Tablebased Fast and Accurate Vector Multiplication in DRAMbased CNN Accelerator. (National Univ. of Defense Technology, Pittsburgh)
 2019 DAC: Successive Log Quantization for CostEfficient Neural Networks Using Stochastic Computing. (UNIST)
 2019 DAC: InStream Stochastic Division and Square Root via Correlation. (WisconsinMadison)
 2019 DATE: EnergyEfficient Convolutional Neural Networks with Deterministic BitStream Processing. (University of Minnesota, University of Louisiana)
 2019 ASPDAC: Logquantized stochastic computing for memory and computation efficient DNNs. (UNIST)
 2019 ASPDAC: Hybrid binaryunary hardware accelerator. (Minnesota)
 2019 TCASI: Efficient CMOS Invertible Logic Using Stochastic Computing. (McGill University, Tohoku University)
 2019 TCASII: New Divider Design for Stochastic Computing.
 2019 TCASII: A stochastic computing architecture for iterative estimation. (Johannes Kepler Universit)
 2019 TCASII: HighAccuracy 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: LowCost Stochastic Hybrid Multiplier for Quantized Neural Networks. (Minnesota, University of Louisiana at Lafayette)
 2019 JETC: Neural Network Classifiers Using a HardwareBased Approximate Activation Function with a Hybrid Stochastic Multiplier. (Minnesota, Rutgers University)
 2019 VLSI System: Design of FSMBased 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 ComputingBased 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 EnergyEfficient and NoiseTolerant Recurrent Neural Network Using Stochastic Computing. (Alberta, Tsinghua, Northeastern) SC+RNN
 2019 ISCAS: Stochastic Computing for LowPower and HighSpeed 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: ContextAware Number Generator for Deterministic Bitstream Computing. (Louisiana at Lafayette)
 2019 ASAP: EnergyEfficient NearSensor Convolution using Pulsed Unary Processing. (Louisiana at Lafayette, Minnesota)
 2019 ASAP: Using Residue Number Systems to Accelerate Deterministic Bitstream Multiplication. (University of Tehran, Louisiana at Lafayette, IPM, Shahid Beheshti University, IROST)
 2019 GLSVLSI: An Efficient Timebased Stochastic Computing Circuitry Employing NeuronMOS. (NAIST)
 2019 GLSVLSI: Low Cost Hybrid SpinCMOS Compressor for Stochastic Neural Networks. (Minnesota)
 2019 ISQED: Accelerating Deterministic BitStream 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: Costeffective stochastic MAC circuits for deep neural networks. (UNIST)
 2019 LCTES: BitBench: a benchmark for bitstream computing. (WisconsinMadison)
 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 MemoryEfficient Neuromorphic Computing. (Purdue)
 2019 ICASSP: Stochastic Datadriven Hardware Resilience to Efficiently Train Inference Models for Stochastic Hardware Implementations. (Princeton) MRAMPIM
 2019 arxiv: Density Encoding Enables ResourceEfficient 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 EnergyEfficient Image Processors. (Kwangwoon University, Hongik University)