NeuroCGRA: A CGRA with support for neural networks

Today, Coarse Grained Reconfigurable Architectures (CGRAs) are becoming an increasingly popular implementation platform. In real world applications, the CGRAs are required to simultaneously host processing (e.g. Audio/video acquisition) and estimation (e.g. audio/video/image recognition) tasks. For estimation problems, neural networks, promise a higher efficiency than conventional processing. However, most of the existing CGRAs provide no support for neural networks. To realize realize both neural networks and conventional processing on the same platform, this paper presents NeuroCGRA. NeuroCGRA allows the processing elements and the network to dynamically morph into either conventional CGRA or a neural network, depending on the hosted application. We have chosen the DRRA as a vehicle to study the feasibility and overheads of our approach. Synthesis results reveal that the proposed enhancements incur negligible overheads (4.4% area and 9.1% power) compared to the original DRRA cell.