Summary of Optimizing the Deployment Of Tiny Transformers on Low-power Mcus, by Victor J.b. Jung et al.
Optimizing the Deployment of Tiny Transformers on Low-Power MCUs
by Victor J.B. Jung, Alessio Burrello, Moritz Scherer, Francesco Conti, Luca Benini
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); 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 This paper proposes a framework for deploying Transformer models on commercial Microcontroller Units (MCUs), which is crucial for real-time processing and edge computing applications. The authors aim to optimize the deployment of Tiny Transformers, a lightweight version of transformer networks, onto single and multi-core MCUs. They introduce a novel inference schedule, Fused-Weight Self-Attention, which fuses linear projection weights offline to reduce operations and parameters. Additionally, they present a Depth-First Tiling scheme for MHSA to mitigate memory peaks reached during attention map computation. The authors evaluate their framework on three different MCU classes, showcasing significant improvements in latency (up to 4.79x) and energy consumption (up to 2.32x lower). This work is particularly relevant for applications such as radar-based hand-gesture recognition, where the proposed framework achieves a latency of 0.14ms and energy consumption of 4.92 micro-joules. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how to make computers really small and powerful so they can do things like recognize hand gestures using radar signals. The authors want to make it easier to use special kinds of computer models called Transformers on these tiny computers, which is important for applications that need to happen quickly, like real-time video processing or self-driving cars. They came up with new ways to make the computer work faster and more efficiently, such as a way to reduce memory usage and another way to speed up calculations. The authors tested their ideas on different types of tiny computers and showed that they can be up to 4.79 times faster and use less energy than other methods. |
Keywords
* Artificial intelligence * Attention * Gesture recognition * Inference * Self attention * Transformer
