☆ 3.8 Proceedings Paper

EACNN: Efficient CNN Accelerator Utilizing Linear Approximation and Computation Reuse

2023 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, ISCAS (2023)

A 12.1 TOPS/W Quantized Network Acceleration Processor With Effective-Weight-Based Convolution and Error-Compensation-Based Prediction

Huiyu Mo et al.

Summary: In this article, a quantized network acceleration processor (QNAP) is proposed to efficiently accelerate CNN processing through a series of optimization schemes, achieving improved energy efficiency and reducing unnecessary operations.

IEEE JOURNAL OF SOLID-STATE CIRCUITS (2022)

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Article Computer Science, Information Systems

Deep Neural Networks-Based Weight Approximation and Computation Reuse for 2-D Image Classification

Mohammed F. Tolba et al.

Summary: This paper introduces a new method to improve the performance of Deep Neural Networks (DNNs) by combining approximate computing and data reuse techniques. This method reduces computational and memory requirements for resource-constrained devices like IoT nodes. Experimental results show that the proposed approach achieves significant improvements in image recognition tasks.

IEEE ACCESS (2022)

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Article Engineering, Electrical & Electronic

An Energy-Efficient Reconfigurable Processor for Binary- and Ternary-Weight Neural Networks With Flexible Data Bit Width

Shouyi Yin et al.

IEEE JOURNAL OF SOLID-STATE CIRCUITS (2019)

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Article Engineering, Electrical & Electronic

Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks

Yu-Hsin Chen et al.

IEEE JOURNAL OF SOLID-STATE CIRCUITS (2017)

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Article Acoustics

Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition

George E. Dahl et al.

IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING (2012)

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EACNN: Efficient CNN Accelerator Utilizing Linear Approximation and Computation Reuse

相关参考文献

A 12.1 TOPS/W Quantized Network Acceleration Processor With Effective-Weight-Based Convolution and Error-Compensation-Based Prediction

Deep Neural Networks-Based Weight Approximation and Computation Reuse for 2-D Image Classification

An Energy-Efficient Reconfigurable Processor for Binary- and Ternary-Weight Neural Networks With Flexible Data Bit Width

Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks

Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition

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