We review HyDRATE, a low-SWaP reconfigurable neural network architecture developed under the DARPA AIE HyDDENN (Hyper-Dimensional Data Enabled Neural Network) program. We describe the training and simulated performance of a feature extractor free of multiply-accumulates (MAC) feeding a hyperdimensional (HD) logic-based classifier and show how performance increases with the number of hyperdimensions. Reconfigurability in the field is achieved by retraining only the feed-forward HD classifier without gradient descent backpropagation. We show performance for a video activity classification task and demonstrate retraining on this same dataset. Finally, we discuss a realized FPGA architecture that achieves 10x smaller memory footprint, 10x simpler operations and 100x lower latency/power compared to traditional deep neural networks.
This paper describes an architecture framework using heterogeneous hardware accelerators for embedded vision applications. This approach leverages the recent single-chip heterogeneous FPGAs that combine powerful multicore processors with extensive programmable gate array fabric on the same die. We present a framework using an extensive library of pipelined real time vision hardware accelerators and a service-based software architecture. This field-proven system design approach provides embedded vision developers with a powerful software abstraction layer for rapidly and efficiently integrating any of hardware accelerators for applications such as image stabilization, moving target indication, contrast normalization enhancement, and others. The framework allows the service-based software to take advantage of the hardware acceleration blocks available and perform the remainder of the processing in software. As performance requirements increase, more hardware acceleration can be added to the FPGA fabric, thus offloading the main processor.