Energy-Efficient Discrete Signal Processing with Field Programmable Analog Arrays (FPAAs)

Large-scale field programmable analog array (FPAA) devices have made analog and analog-digital signal processing techniques accessible to a much wider community. However, largely due to its severe resource constraints, high noise sensitivity, and enormous design space, reconfigurable analog computing remains a niche in the DSP application space. In this paper, we develop a probabilistic-based methodology for designing and implementing the analog computing engines that specifically target at energy-efficient signal processing systems. We will first demonstrate how to decompose a given DSP application into various functional modules within the framework of probabilistic-based processing. Furthermore, we will show how these individual functional modules can be easily mapped to the limited selection of analog blocks found in an commercially available FPAA device: the PSoC chip platform from Cypress. To keep our study concrete, our implementation example focuses on the 1-D convolution module, a fundamental algorithmic building block in many applications of computer vision and artificial intelligence. In the end, we construct a complete image processing system based on the PSoC chip platform, and use the application of image key point extraction to demonstrate that our proposed approach to reconfigurable analog computing has considerable advantages in hardware usage, energy efficiency, and computing robustness over the traditional DSP approaches.