Memory-efficient Energy-adaptive Inference of Pre-Trained Models on Batteryless Embedded Systems

• URL: http://arxiv.org/abs/2405.10426v1
• Date: Thu, 16 May 2024 20:16:45 GMT
• Title: Memory-efficient Energy-adaptive Inference of Pre-Trained Models on Batteryless Embedded Systems
• Authors: Pietro Farina, Subrata Biswas, Eren Yıldız, Khakim Akhunov, Saad Ahmed, Bashima Islam, Kasım Sinan Yıldırım,
• Abstract summary: Batteryless systems often face power failures, requiring extra runtime buffers to maintain progress and leaving only a memory space for storing ultra-tiny deep neural networks (DNNs) We propose FreeML, a framework to optimize pre-trained DNN models for memory-efficient and energy-adaptive inference on batteryless systems. Our experiments showed that FreeML reduces the model sizes by up to $95 times$, supports adaptive inference with a $2.03-19.65 times$ less memory overhead, and provides significant time and energy benefits with only a negligible accuracy drop compared to the state-of-the-art
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Batteryless systems frequently face power failures, requiring extra runtime buffers to maintain inference progress and leaving only a memory space for storing ultra-tiny deep neural networks (DNNs). Besides, making these models responsive to stochastic energy harvesting dynamics during inference requires a balance between inference accuracy, latency, and energy overhead. Recent works on compression mostly focus on time and memory, but often ignore energy dynamics or significantly reduce the accuracy of pre-trained DNNs. Existing energy-adaptive inference works modify the architecture of pre-trained models and have significant memory overhead. Thus, energy-adaptive and accurate inference of pre-trained DNNs on batteryless devices with extreme memory constraints is more challenging than traditional microcontrollers. We combat these issues by proposing FreeML, a framework to optimize pre-trained DNN models for memory-efficient and energy-adaptive inference on batteryless systems. FreeML comprises (1) a novel compression technique to reduce the model footprint and runtime memory requirements simultaneously, making them executable on extremely memory-constrained batteryless platforms; and (2) the first early exit mechanism that uses a single exit branch for all exit points to terminate inference at any time, making models energy-adaptive with minimal memory overhead. Our experiments showed that FreeML reduces the model sizes by up to $95 \times$, supports adaptive inference with a $2.03-19.65 \times$ less memory overhead, and provides significant time and energy benefits with only a negligible accuracy drop compared to the state-of-the-art.

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