Fast-Inf: Ultra-Fast Embedded Intelligence on the Batteryless Edge
Authors: 
Leonardo Lucio Custode, Pietro Farina, 
Eren Yildiz, Renan Beran Kilic, 
Kasim Sinan Yildirim, Giovanni IaccaAuthors Info & Claims
Leonardo Lucio Custode, Pietro Farina, Eren Yildiz, Renan Beran Kilic, Kasim Sinan Yildirim, Giovanni IaccaAuthors Info & ClaimsPages 239 - 252
Abstract
Batteryless edge devices are extremely resource-constrained compared to traditional mobile platforms. Existing tiny deep neural network (DNN) inference solutions are problematic due to their slow and resource-intensive nature, rendering them unsuitable for batteryless edge devices. To address this problem, we propose a new approach to embedded intelligence, called Fast-Inf, which achieves extremely lightweight computation and minimal latency. Fast-Inf uses binary tree-based neural networks that are ultra-fast and energy-efficient due to their logarithmic time complexity. Additionally, Fast-Inf models can skip the leaf nodes when necessary, further minimizing latency without requiring any modifications to the model or retraining. Moreover, Fast-Inf models have significantly lower backup and runtime memory overhead. Our experiments on an MSP430FR5994 platform showed that Fast-Inf can achieve ultra-fast and energy-efficient inference (up to 700x speedup and reduced energy) compared to a conventional DNN.
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Index Terms
- Fast-Inf: Ultra-Fast Embedded Intelligence on the Batteryless Edge
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Information & Contributors
Information
Published In
November 2024
950 pages
ISBN:9798400706974
DOI:10.1145/3666025
- Chair:
- Jie Liu,
- Co-chairs:
- Yuanchao Shu,
- Jiming Chen,
- Program Chair:
- Yuan He,
- Program Co-chair:
- Rui Tan
This work is licensed under a Creative Commons Attribution International 4.0 License.
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- SIGARCH: ACM Special Interest Group on Computer Architecture
- SIGBED: ACM Special Interest Group on Embedded Systems
- SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
- SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
- SIGOPS: ACM Special Interest Group on Operating Systems
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Published: 04 November 2024
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SenSys '24
SenSys '24: 22nd ACM Conference on Embedded Networked Sensor Systems
November 4 - 7, 2024
Hangzhou, China
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Overall Acceptance Rate 174 of 867 submissions, 20%
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