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Stochastic-HD: Leveraging Stochastic Computing on the Hyper-Dimensional Computing Pipeline.

Justin Morris1, Yilun Hao2, Saransh Gupta3

  • 1Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA, United States.

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PubMed
Summary
This summary is machine-generated.

Stochastic-HD enhances brain-inspired Hyper-dimensional (HD) computing in Processing-in-Memory (PIM) by using Stochastic Computing (SC). This novel approach removes bottlenecks, boosting efficiency and accuracy for advanced AI tasks.

Keywords:
Hyper-dimensional computingbrain inspired cognitive architecturemachine learningprocessing in memorystochastic computing

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Hardware Architecture

Background:

  • Brain-inspired Hyper-dimensional (HD) computing offers efficient computation but faces bottlenecks in parallel architectures like Processing-in-Memory (PIM) due to reduction operations.
  • Existing PIM designs struggle with the computational demands of HD algorithms, limiting throughput and efficiency.

Purpose of the Study:

  • To introduce Stochastic-HD, a novel computing paradigm combining Stochastic Computing (SC) with HD computing to overcome PIM bottlenecks.
  • To develop an in-memory hardware design for Stochastic-HD that leverages bitwise operations for enhanced parallelism and energy efficiency.
  • To enable efficient implementation of HD Clustering within the Stochastic-HD framework.

Main Methods:

  • Implemented HD operations using deterministic SC, enabling parallel bitwise operations and eliminating reduction bottlenecks.
  • Designed an in-memory hardware architecture utilizing bitwise and associative memory-like operations for fast and energy-efficient processing.
  • Developed an integrated Stochastic-HD retraining approach and accelerated retraining within the hardware for an end-to-end accelerator.
  • Extended Stochastic-HD to support HD Clustering operations, mapping them to the stochastic domain.

Main Results:

  • Stochastic-HD achieved comparable accuracy to Baseline-HD, with accuracy loss reduced to 0.3% through integrated retraining.
  • The proposed hardware design demonstrated high parallelism and robustness to approximation.
  • Stochastic-HD outperformed the best PIM design for HD by 4.4% in accuracy and 43.1x in energy efficiency.
  • Successfully mapped HD Clustering operations to the stochastic domain for the first time.

Conclusions:

  • Stochastic-HD effectively removes reduction operation bottlenecks in PIM for HD computing, significantly improving throughput.
  • The developed in-memory hardware provides a fast, energy-efficient, and accurate solution for HD computing tasks, including clustering.
  • Stochastic-HD represents a significant advancement in efficient AI hardware, offering superior performance and energy efficiency compared to existing PIM designs.