Harvesting AI Computation at the Edge via Generic Approximation
With the widespread adoption of AI in various IoT scenarios such as smart sensing and processing, AI chips have become a common component at the edge. These chips are typically specialized for structured neural network (NN) processing and are designed to meet peak workload demands. However, they are often underutilized and suffer from considerable computational waste due to temporal or spatial redundancy in processing. Conversely, general-purpose processing engines at the edge may struggle with compute-intensive tasks such as signal processing and complex numerical operations because of stringent resource constraints. To address this imbalance, we propose a framework that harvests unused ...
| Authors | Yihan Wang, Huiru Yan, Luxin Zhang et al. |
|---|---|
| Primary category | cs.AR |
| Published | 28 June 2026 |
| Last updated at source | 28 June 2026 |
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