An FPGA-based accelerator for cortical object classification

Recently significant advances have been achieved in understanding the visual information processing in the human brain. The focus of this work is on the design of an architecture to support HMAX, a widely accepted model of the human visual pathway. The computationally intensive nature of HMAX and wide applicability in real-time visual analysis application makes the design of hardware accelerators a key necessity. In this work, we propose a configurable accelerator mapped efficiently on a FPGA to realize real-time feature extraction for vision-based classification algorithms. Our innovations include the efficient mapping of the proposed architecture on the FPGA as well as the design of an efficient memory structure. Our evaluation shows that the proposed approach is significantly faster than other contemporary solutions on different platforms.