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Algorithm/Accelerator Co-Design and Co-Search for Edge AI
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AI
DOI：10.1109/TCSII.2022.3179229.png)
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Abstract
En 中文
The world has seen the great success of deep neural networks (DNNs) in a massive number of artificial intelligence (AI) applications. However, developing high-quality AI services to satisfy diverse real-life edge scenarios still encounters many difficulties. As DNNs become more compute- and memory-intensive, it is challenging for edge devices to accommodate them with limited computation/memory resources, tight power budgets, and small form-factors. Challenges also come from the demanding requirements of edge AI, requesting real-time responses, high-throughput performance, and reliable inference accuracy. To address these challenges, we propose a series of efficient design methods to perform algorithm/accelerator co-design and co-search for optimized edge AI solutions. We demonstrate our proposed methods on popular edge AI applications (object detection and image classification) and achieve significant improvements than prior designs.
Keywords:
Hardware
Artificial intelligence
Image edge detection
Software algorithms
Software
Computer architecture
Tensors
AI accelerators
HW
SW co-design
edge computing
deep neural network
Journal
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IF:
4.9
Papers:
8.8K
Citations:
2.5W
