We held our next tinyML Talks webcast. Amit Mate from GMAC Intelligence has presented AI/ML solutions for low-power Edge platforms - challenges and opportunities on October 17, 2020.
Edge comprises a myriad of devices spanning micro-controller based, application processor based and server based. These are consumer electronic devices ranging from drones, small-robots, surveillance cameras, AR/VR goggles, wearables and many more. The compute type ranges from CPU, GPU, DSP, NPU and uC. These devices have different OSes such as custom-linux, ubuntu, android and ROS to name a few. The on-device acceleration libraries range from pytorch, tensorflow, tensorRT and such. What should an application developer do to enable a use-case? Too many choices, complexities in a compute, storage, memory and power limited Edge environment. We are building an on-device AI/ML library and API that lets application-developers build any conceivable AI/ML application with ease and also enables on-device training of few NN layers right on the Edge.
Amit Mate has 20+ years of experience in leading cross-functional Engineering teams on ML and Wireless projects from concept through commercialization. He has delivered commercial grade software on several deep-technologies ( 3G/4G, OCR, VR, Femtocells) with Industry leaders such as Qualcomm and Nokia. Amit earned his master’s degree in electrical communication engineering from IISc, Bangalore and bachelor’s in electronics and communication from NIT, Nagpur. He has been awarded 10+ patents including 3GPP essential patents.
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