The challenges involved in executing neural networks (NNs) at the edge include providing diversity, flexibility, and sustainability. That implies, for instance, supporting evolving applications and algorithms energy-efficiently. Using hardware or software accelerators can deliver fast and efficient computation of the NNs, while flexibility can be exploited to support long-term adaptivity. Nonetheless, handcrafting an NN for a specific device, despite the possibility of leading to an optimal solution, takes time and experience, and that's why frameworks for hardware accelerators are being developed. This work, starting from a preliminary semi-integrated ONNX-to-hardware toolchain [21], focuses on enabling approximate computing leveraging the distinctive ability of the original toolchain to favor adaptivity. The goal is to allow lightweight adaptable NN inference on FPGAs at the edge.

翻译：在边缘设备上执行神经网络所面临的挑战包括提供多样性、灵活性与可持续性。这意味着需要以高能效方式支持不断演进的应用与算法。采用硬件或软件加速器可实现神经网络的快速高效计算，而灵活性则可用于支持长期适应性。尽管为特定设备手工定制神经网络可能获得最优解，但这一过程需要大量时间与专业知识，这正是当前硬件加速器框架持续发展的动因。本研究基于前期半集成式ONNX至硬件工具链[21]，重点利用原始工具链支持适应性的独特能力实现近似计算。其目标在于实现边缘FPGA设备上轻量级自适应神经网络推理。