- Get link
- Other Apps
This coming Monday, Perry Mulligan and COO Sumit Sharma will be presenting at Northland Capital Markets’ Artificial Intelligence Virtual Investor Call Series.
Apart from a sense of interest in my first time hearing Sharma present, I'll be keenly interested to learn about MVIS' position and the use cases for its technology in AI, which is (as I am beginning to understand it) to enable "AI at the Edge" through sensing and local (on-device) recognition. MVIS AI at the Edge will be incorporated in their display and sensing modules and is intended for use in AI smart speakers, home security systems, augmented reality and automotive LIDAR for autonomous driving.
Earlier today I posted a link to a new AI Edge Device market research paper that describes AI Edge Devices going from 161.4 million units in 2018 to 2.6 billion units worldwide annually in 2025.
AI edge processing today is focused on moving the inference part of the AI workflow to the device, keeping data constrained to the device. There are several different reasons why AI processing is moving to the edge device, depending on the application. Privacy, security, cost, latency, and bandwidth all need to be considered when evaluating cloud versus edge processing. The impact of model compression techniques like Google’s Learn2Compress that enables squeezing large AI models into small hardware form factors is also contributing to the rise of AI edge processing. Federated learning and blockchain-based decentralized AI architectures are also part of the shift of AI processing to the edge with part of the training also likely to move to the edge. Depending on the AI application and device category, there are several hardware options for performing AI edge processing. These options include CPUs, GPUs, ASICs, FPGAs, and SoC accelerators.
MVIS stated on the Q2 CC July 30 that their LIDAR module development kits would be available for customers in Q4. I wonder if these LIDAR kits will include new hardware to support on-device learning and recognition, or what the incorporation of the MVIS AI at the Edge functionality is going to require.
Hopefully on Monday's call we will learn what MVIS plans to offer in the AI at the Edge space, how their AI/machine learning capability is embodied and what some of the use cases are where we expect the MVIS solution to be differentiated.
Apart from a sense of interest in my first time hearing Sharma present, I'll be keenly interested to learn about MVIS' position and the use cases for its technology in AI, which is (as I am beginning to understand it) to enable "AI at the Edge" through sensing and local (on-device) recognition. MVIS AI at the Edge will be incorporated in their display and sensing modules and is intended for use in AI smart speakers, home security systems, augmented reality and automotive LIDAR for autonomous driving.
Earlier today I posted a link to a new AI Edge Device market research paper that describes AI Edge Devices going from 161.4 million units in 2018 to 2.6 billion units worldwide annually in 2025.
AI edge processing today is focused on moving the inference part of the AI workflow to the device, keeping data constrained to the device. There are several different reasons why AI processing is moving to the edge device, depending on the application. Privacy, security, cost, latency, and bandwidth all need to be considered when evaluating cloud versus edge processing. The impact of model compression techniques like Google’s Learn2Compress that enables squeezing large AI models into small hardware form factors is also contributing to the rise of AI edge processing. Federated learning and blockchain-based decentralized AI architectures are also part of the shift of AI processing to the edge with part of the training also likely to move to the edge. Depending on the AI application and device category, there are several hardware options for performing AI edge processing. These options include CPUs, GPUs, ASICs, FPGAs, and SoC accelerators.
MVIS stated on the Q2 CC July 30 that their LIDAR module development kits would be available for customers in Q4. I wonder if these LIDAR kits will include new hardware to support on-device learning and recognition, or what the incorporation of the MVIS AI at the Edge functionality is going to require.
Hopefully on Monday's call we will learn what MVIS plans to offer in the AI at the Edge space, how their AI/machine learning capability is embodied and what some of the use cases are where we expect the MVIS solution to be differentiated.
Comments
Post a Comment