Gordon Hands, Director of Marketing for IP and Solutions at Lattice Semiconductor, presents the "Programmable CNN Acceleration in Under 1 Watt" tutorial at the May 2018 Embedded Vision Summit.
Driven by factors such as privacy concerns, limited network bandwidth and the need for low latency, system designers are increasingly interested in implementing artificial intelligence (AI) at the edge. Low-power (under 1 Watt), low-cost (under $10) FPGAs, such as Lattice’s ECP5, offer an attractive option for implementing AI at the edge. In order to achieve the best balance of accuracy, power and performance, designers need to carefully select the network model and quantization level. This presentation uses two application examples to help system architects better understand feasible solutions. These examples illustrate the trade-offs of network design, quantization and performance.