As AI algorithms become more complex, they consume disproportionately greater run-time and energy. This makes meeting performance or efficiency goals require some level of hardware acceleration. The highest levels of performance and efficiency are achieved with custom hardware. Traditional hardware design and verification methodologies are labor-intensive and time-consuming and are not a good match for rapidly evolving AI technologies.
We will introduce the application of High-Level Synthesis (HLS) for automating many of the hardware design tasks involved in creating a bespoke accelerator. Using HLS, popular machine learning frameworks, and Quantize-Aware Training, we can build highly optimized and bit precise hardware, targeting ASIC or FPGA implementations.
Cameron Villone
Cameron Villone is a High-Level Synthesis Technologist working with the Catapult High-Level Synthesis product management and marketing team, focusing on AI hardware deployment. He previously worked as a product marketing engineer for PowerPro, Siemens’ power optimization and analysis product. He held previous roles at Texas Instruments and General Motors. Cameron studied and graduated from Rochester Institute of Technology, obtaining both a bachelor's and master's degree in electrical engineering, focusing on Robotics, Embedded Systems, and Computer Vision.