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This is a general-purpose simulator for unary computing based on PyTorch, with the paper accepted to ISCA 2020 and awarded IEEE Micro Top Pick for 2020.

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Unary Computing

Unary computing is a type of computing paradigm based on serial bits in streams, rather than parallel bits in conventional binary computing. The unary computing kernels are much simpler than conventional binary counterparts, but spend more cycles for a single operation.

In general, a single unary computing kernel may not outperform the binary counterpart in terms of energy efficiency. However, at the system level, there are two advantages in unary computing over binary computing with respect to the system input.

  1. If the system input is analog from sensors, unary computing eliminates the process of converting the analog signals to binary format and storing it, reducing the energy consumption [1, 2].
  2. If the system input is already binary, due to the higher computing density resulting from simple logic, unary computing can achieve even higher energy efficiency than binary computing when the computational intensity reaches a certain threshold [3].

UnarySim

This UnarySim is a PyTorch-based cycle-accurate simulator for large scale unary computing, compatible to both CPU and GPU with high efficiency.

The paper related to this simulator, "uGEMM: Unary Computing Architecture for GEMM Applications", is accepted to International Symposium on Computer Architecture (ISCA) 2020, and further awarded IEEE Micro Top Pick from 2020 Computer Architecture Conferences.

The components included in UnarySim belong to three categories, including

  1. Stream Manipulation
  2. Computing Kernel
  3. Performance Metric

Among those, components in Stream Manipulation and Computing Kernel can physically exist in real hardware, while those in Performance Metric are virtually for performance analysis.

Prerequisite

  1. PyTorch (Version >= 1.10)
  2. Python (Version >= 3.6)
  3. pylfsr
  4. numpy
  5. torchinfo
  6. matplotlib

Environment configuration before simulation: export PYTHONPATH=<UnarySim-Parent-Dir-Absolute-Path>/

Data Representation

UnarySim has five categories of data, with each having default data type as 'torch.float' in PyTorch.

  1. Source Data: The input source data in unary computing need to be ranging from 0 to 1 in unipolar format (Uni), or from -1 to 1 in bipolar format (Bi). The input source data (source) is scaled to a certain range (as in unipolar/bipolar format) from the raw data (raw) with respect to its maximum. An example scaling is source = raw / max(raw).

  2. Random Number: The random numbers (rand) are to be compared with the source data in order to generate the bitstreams. To notice, source data is in unipolar/bipolar format, while random numbers are integers. To compare them, source data requires to scale up by the bitwidth of random numbers. At each cycle, if round(source * 2^bitwidth) > rand, a bit of logic 1 in the stream will be generated; otherwise, a bit of logic 0 will be generated.

  3. Stream: At each cycle, the bitstreams physically flow through cascaded logic kernels, and they count for most of the memory space during simulation. The bitstream can leverage either rate coding (RC) or temporal coding (TC).

  4. Buffer: Inside each logic kernel, there may exist buffers, like counters or shift registers, to compute future data by monitoring the past bitstreams.

  5. Metric Variable: Those are variables to compute specially designed performance metrics.

Directory Hierarchy

This directory contains multiple subdirectories, including 'stream', 'kernel', 'metric', 'test', 'app', 'hw', covering three components mentioned above.

'stream' subdirectory

This directory contains the components for Stream Manipulation, which manipulate the bitstreams for high performance and accuracy.

Name Date Encoding Polarity Reference Status
Bi2Uni Mar 31, 2020 RC Bi [11]
BSGen Seq 07, 2019 Both Both [4]
BSGenMulti Nov 11, 2019 Both Both [4]
RawScale Dec 07, 2019 Both Both NA
RNG Seq 07, 2019 Both Both [5]
RNGMulti Nov 11, 2019 Both Both [5]
SourceGen Seq 07, 2019 Both Both [4]
SkewedSync Seq 07, 2019 Both Both [6]
Uni2Bi Mar 31, 2020 RC Uni [11]

'kernel' subdirectory

This directory contains the components for Computing Kernel, which take bitstreams as inputs and perform actual unary computation. The supported kernels are listed as follows. The components currently supported or to be implemented are listed in the table below.

  1. Fully Streaming Unary (FSU) components that both consume and produce unary bitstreams.
Name Date Encoding Polarity Reference Status
FSUAbs Mar 25, 2020 RC Bi [11]
FSUAdd Oct 10, 2019 Both Both [7]
FSUConv2d Jun 02, 2021 Both Both NA
FSUDiv Apr 01, 2020 RC Both [6]
FSUHardsigmoid Jun 06, 2021 RC Both NA
FSUHardtanh Jun 06, 2021 RC Both NA
FSULinear Seq 27, 2019 Both Both [7]
FSUMGUCell Jun 06, 2021 RC Both NA
FSUMul Nov 05, 2019 RC Both [7]
FSUReLU Nov 23, 2019 Both Both [7]
FSUSign Mar 31, 2020 RC Bi [11]
FSUSqrt Apr 02, 2020 RC Both [6]
  1. Hybrid Unary Binary (HUB) components that both consume and produce binary data but compute on bitstreams.
Name Date Encoding Polarity Reference Status
HUBConv2d Jun 02, 2021 Both Both [12]
HUBLinear Jun 02, 2021 Both Both [12]
  1. Useful sub-components.
Name Date Encoding Polarity Reference Status
CORDIV_kernel Mar 08, 2020 RC Uni [6]
GainesAdd Mar 02, 2020 Both Both [4]
GainesDiv Mar 08, 2020 RC Both [4]
GainesLinear Nov 25, 2019 RC Both [4]
GainesMul Dec 06, 2019 RC Both [4]
GainesSqrt Mar 24, 2020 RC Both [4]
JKFF Apr 01, 2020 NA NA NA
ShiftReg Dec 06, 2019 NA NA NA

'metric' subdirectory

This directory contains the components for Performance Metric, which take bit streams as input and calculate certain performance metrics. The components currently supported or to be implemented are listed in the table below.

Name Date Encoding Polarity Reference Status
Correlation Seq 07, 2019 Both Both [8]
NormStability Dec 18, 2019 Both Both [10]
NSBuilder Mar 31, 2020 Both Both [10]
ProgError Seq 07, 2019 Both Both [9]
Stability Dec 27, 2019 Both Both [7]

'test' subdirectory

This directory contains simple testing examples for above components.

'app' subdirectory

This directory contains several applications implemented using this UnarySim.

'hw' subdirectory

This directory includes the hardware implementation of components in Stream Manipulation and Computing Kernel, and is still in progress.

Reference

[1] V. T. Lee, A. Alaghi, J. P. Hayes, V. Sathe, and L. Ceze, "Energy-efficient hybrid stochastic-binary neural networks for near-sensor computing", in DATE 2017. [2] S. K. Khatamifard, M. H. Najafi, A. Ghoreyshi, U. R. Karpuzcu and D. J. Lilja, "On Memory System Design for Stochastic Computing", in IEEE Computer Architecture Letters 2018. [3] V. T. Lee, A. Alaghi, R. Pamula, V. S. Sathe, L. Ceze and M. Oskin, "Architecture Considerations for Stochastic Computing Accelerators", in TCAD 2018.
[4] B.R. Gaines, "Stochastic computing systems," in Advances in Information Systems Science 1969.
[5] S. Liu and J. Han, "Energy efficient stochastic computing with Sobol sequences," in DATE 2017.
[6] D. Wu and J. S. Miguel, "In-Stream Stochastic Division and Square Root via Correlation," in DAC 2019.
[7] D. Wu, J. Li, R. Yin, H. Hsiao, Y. Kim and J. San Miguel, "uGEMM: Unary Computing Architecture for GEMM Applications," in ISCA 2020.
[8] A. Alaghi and J. P. Hayes, "Exploiting correlation in stochastic circuit design," in ICCD 2013.
[9] A. Alaghi and J. P. Hayes, "Fast and accurate computation using stochastic circuits," in DATE 2014.
[10] D. Wu, R. Yin and J. San Miguel, "Normalized Stability: A Cross-Level Design Metric for Early Termination in Stochastic Computing", in ASP-DAC 2021.
[11] D. Wu, R. Yin and J. San Miguel, "In-Stream Correlation-Based Division and Bit-Inserting Square Root in Stochastic Computing", in IEEE Design & Test 2021.
[12] D. Wu and J. San Miguel, "uSystolic: Byte-Crawling Unary Systolic Array," in HPCA 2022.

Citing

If you find UnarySim is useful for your research, please use the following bibtex to cite us,

@inproceedings{diwu2020uGEMM,
  title = {{uGEMM: Unary Computing Architecture for GEMM Applications}},
  author = {Di Wu and Jingjie Li and Ruokai Yin and Hsuan Hsiao and Younghyun Kim and Joshua San Miguel},
  booktitle = {International Symposium on Computer Architecture (ISCA)},
  year = {2020},
}

Contribution

Active contributor:

  1. Di Wu
  2. Ruokai Yin
  3. Jingjie Li
  4. Zhewen Pan

Please contact me (di.wu@ece.wisc.edu) if you are interested in contributing to this project!

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This is a general-purpose simulator for unary computing based on PyTorch, with the paper accepted to ISCA 2020 and awarded IEEE Micro Top Pick for 2020.

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