Summary of Usefuse: Utile Stride For Enhanced Performance in Fused Layer Architecture Of Deep Neural Networks, by Muhammad Sohail Ibrahim and Muhammad Usman and Jeong-a Lee
USEFUSE: Utile Stride for Enhanced Performance in Fused Layer Architecture of Deep Neural Networks
by Muhammad Sohail Ibrahim, Muhammad Usman, Jeong-A Lee
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Hardware Architecture (cs.AR); Performance (cs.PF)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Sum-of-Products (SOP) units for convolution in Convolutional Neural Networks (CNNs) utilize low-latency left-to-right bit-serial arithmetic to minimize response time and enhance overall performance. The methodology fuses multiple convolution layers to reduce off-chip memory communication and increase overall performance, while an effective mechanism detects and skips inefficient convolutions after ReLU layers to minimize power consumption without compromising accuracy. Additionally, efficient tile movement guarantees uniform access to the fusion pyramid and the utile stride strategy improves operational intensity. Two designs cater to varied demands: one focuses on minimal response time for mission-critical applications and another focuses on resource-constrained devices with comparable latency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making Convolutional Neural Networks (CNNs) work better on devices that don’t have a lot of power or memory. The researchers came up with new ways to do convolutions, which are a crucial part of CNNs. They used special math tricks called left-to-right bit-serial arithmetic to make things run faster and use less energy. They also figured out how to combine multiple layers together without using too much extra memory. This helps the network work better on edge devices like smartphones or smart home devices. The researchers tested their ideas and found that they really do make a difference in terms of speed and efficiency. |
Keywords
» Artificial intelligence » Relu
