Distributed Inference with Minimal Off-Chip Traffic for Transformers on Low-Power MCUs
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916662587949056 |
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| author | Bochem, Severin Jung, Victor J. B. Prasad, Arpan Conti, Francesco Benini, Luca |
| author_facet | Bochem, Severin Jung, Victor J. B. Prasad, Arpan Conti, Francesco Benini, Luca |
| contents | Contextual Artificial Intelligence (AI) based on emerging Transformer models is predicted to drive the next technology revolution in interactive wearable devices such as new-generation smart glasses. By coupling numerous sensors with small, low-power Micro-Controller Units (MCUs), these devices will enable on-device intelligence and sensor control. A major bottleneck in this class of systems is the small amount of on-chip memory available in the MCUs. In this paper, we propose a methodology to deploy real-world Transformers on low-power wearable devices with minimal off-chip traffic exploiting a distributed system of MCUs, partitioning inference across multiple devices and enabling execution with stationary on-chip weights. We validate the scheme by deploying the TinyLlama-42M decoder-only model on a system of 8 parallel ultra-low-power MCUs. The distributed system achieves an energy consumption of 0.64 mJ, a latency of 0.54 ms per inference, a super-linear speedup of 26.1 x, and an Energy Delay Product (EDP) improvement of 27.2 x, compared to a single-chip system. On MobileBERT, the distributed system's runtime is 38.8 ms, with a super-linear 4.7 x speedup when using 4 MCUs compared to a single-chip system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_04372 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Distributed Inference with Minimal Off-Chip Traffic for Transformers on Low-Power MCUs Bochem, Severin Jung, Victor J. B. Prasad, Arpan Conti, Francesco Benini, Luca Hardware Architecture Contextual Artificial Intelligence (AI) based on emerging Transformer models is predicted to drive the next technology revolution in interactive wearable devices such as new-generation smart glasses. By coupling numerous sensors with small, low-power Micro-Controller Units (MCUs), these devices will enable on-device intelligence and sensor control. A major bottleneck in this class of systems is the small amount of on-chip memory available in the MCUs. In this paper, we propose a methodology to deploy real-world Transformers on low-power wearable devices with minimal off-chip traffic exploiting a distributed system of MCUs, partitioning inference across multiple devices and enabling execution with stationary on-chip weights. We validate the scheme by deploying the TinyLlama-42M decoder-only model on a system of 8 parallel ultra-low-power MCUs. The distributed system achieves an energy consumption of 0.64 mJ, a latency of 0.54 ms per inference, a super-linear speedup of 26.1 x, and an Energy Delay Product (EDP) improvement of 27.2 x, compared to a single-chip system. On MobileBERT, the distributed system's runtime is 38.8 ms, with a super-linear 4.7 x speedup when using 4 MCUs compared to a single-chip system. |
| title | Distributed Inference with Minimal Off-Chip Traffic for Transformers on Low-Power MCUs |
| topic | Hardware Architecture |
| url | https://arxiv.org/abs/2412.04372 |