Machine learning inference has thoroughly penetrated embedded applications, especially with visual and spatial data like images and radar/lidar. Texas Instruments has a scalable processor portfolio for vision inference at the edge. From inexpensive Arm®-only solutions to computationally-efficient hardware accelerated solutions that deliver optimum combinations of performance, power efficiency and cost, our device performance levels range from 0.5 TOPS to 8 TOPS. With a large collection of pre-optimized AI models, no-cost and low-cost development tools, and a hardware-agnostic software programming environment, TI helps you bring your idea to realization on an embedded device in no time.
Webinar topics include:
• Scalable processor performance from 0.5 TOPS to 8 TOPS
• No-cost and low-cost development tools
• TI Model Zoo with free optimized models
• Hardware agnostic programming
• Edge Impulse partnership
Systems Applications Engineer, Texas Instruments (TI)
Reese Grimsley is a Systems Applications Engineer at Texas Instruments. Reese holds a Bachelor's degree in Electrical Engineering from Texas A&M University and a Master's in Electrical and Computer Engineering from Carnegie Mellon where he focused...