  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.
