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Deep Neural Networks for Image-Based Dietary Assessment
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Accelerating Inference of Convolutional Neural Networks Using In-memory Computing.

Martino Dazzi1,2, Abu Sebastian1, Luca Benini2

  • 1IBM Research Europe, Rüschlikon, Zurich, Switzerland.

Frontiers in Computational Neuroscience
|August 20, 2021
PubMed
Summary
This summary is machine-generated.

In-memory computing (IMC) offers energy-efficient deep learning hardware. This study details architectural designs for IMC-based Convolutional Neural Networks (CNNs), achieving superior performance for image classification.

Keywords:
AI hardwarecomputational memoryconvolutional neural networkin-memory computingneural network acceleration

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Area of Science:

  • Computer Engineering
  • Artificial Intelligence Hardware

Background:

  • In-memory computing (IMC) is a non-von Neumann architecture.
  • IMC offers energy-efficient, high-throughput processing for deep learning.
  • Existing IMC hardware requires architectural redesign for deep learning tasks.

Purpose of the Study:

  • To develop application-specific IMC hardware for Convolutional Neural Network (CNN) inference.
  • To provide methodologies for implementing IMC core architectural components.
  • To optimize IMC for energy efficiency and high throughput in deep learning.

Main Methods:

  • Mapping synaptic weights and activations onto IMC memory structures.
  • Analyzing trade-offs between on-chip memory and execution latency.
  • Implementing a pipelined dataflow for IMC-based CNN inference.

Main Results:

  • Demonstrated methodologies for IMC architectural components.
  • Quantified trade-offs in memory requirements and latency.
  • Achieved state-of-the-art throughput and latency for image classification.

Conclusions:

  • IMC provides a viable, high-performance architecture for CNN inference.
  • Architectural design is crucial for maximizing IMC efficiency.
  • The proposed methods enable beyond state-of-the-art performance in deep learning applications.