Our methodology efficiently discovers an interpretable and generalizable zero-cost proxy that gives state of the art score-accuracy correlation on all datasets and search spaces of NASBench-201 and Network Design Spaces (NDS).
Neural networks are becoming increasingly ubiquitous in a wide range of use cases.
The rapidly evolving field of Artificial Intelligence necessitates automated approaches to co-design neural network architecture and neural accelerators to maximize system efficiency and address productivity challenges.
We focus on how to design topologies that complement such a view of neurons, how to automate such a strategy of neural network design, and inference of such networks on Xilinx FPGAs.
Deployment of deep neural networks for applications that require very high throughput or extremely low latency is a severe computational challenge, further exacerbated by inefficiencies in mapping the computation to hardware.