The weather has fined up and Brainchip has won the toss and to take advantage of what looks from up here to be a pretty good batting wicket has elected to bat. Opening for Brainchip will be AKD1000 and the IP. We go down to our on field commentator Tata Elxsi for a wicket condition report. Over to you Tata:
Edge AI Solutions
Innovate|Design|Scale
“Edge AI is here to stay! Artificial intelligence (AI) is powering many real-world applications which we see in our daily lives. AI, once seen as an emerging technology, has now successfully penetrated every industry (B2B & B2C) Banking, logistics, healthcare, defense, manufacturing, retail, automotive, consumer electronics. Smart Speaker like Echo, Google Nest, is one such example of Edge AI solutions in the consumer electronics sector.
AI technology is powerful, and human-kind has set its eye on the path of harnessing its potential to the fullest. Intelligence brought to the device can be very useful and creative.
The key requirements that need to be factored in designing Edge AI architecture are — bandwidth, latency, privacy, security, power consumption. While envisioning an Edge AI solution, these requirements need to be thoroughly weighed in terms of what feature can be traded off and yet be effective.
AI enables the machines to perform cognitive functions such as perceiving, reasoning, and learning similar to humans but much faster and accurately. AI implementation is majorly classified into two phases — Learning and Inference.
Globally, the AI chipset market size is expected to be valued at USD 7.6 billion in 2020 and likely to reach USD 57.8 billion by 2026, at a CAGR of 40.1% during this period. Implementation of AI is the current trend in chip technology, and it’s going to stay that way. Many leading semiconductor companies and venture capitalists see it as the right tech-front for investment.
Opportunities & Challenges
Changing dynamics in terms of hardware consideration for learning and inference has led to the Edge AI hardware market being segmented into CPU, GPU, ASIC, and FPGA. ASICs enable high processing capability with low-power consumption, making them perfectly suited for Edge devices in many applications. It is estimated that an Edge AI architecture inference implemented on ASIC will grow from 30% to 70% and 20% on GPU by 2025. Edge devices are embedded products with resource constraints, and hence, Edge AI implementation needs to be thought of as an application-specific use case. AI-based applications for Edge devices are intelligent robots, autonomous vehicles, smart home appliances, among others. The primary applications that run over Edge AI are related to image/video, sound, and speech, natural language processing, device control system, and high-volume computing.
The global Edge AI software market is estimated to cross $3 Billion by 2027. At this juncture of technology innovations, Semiconductor companies enable AI solutions to realize newer strategies to grow their business and find wider hardware adoption. Many semiconductor companies are no longer seen as just component providers but as complete platform solution providers. Semiconductor companies realize value gained from software and services associated with the chipsets that allow for the rapid adoption of their platforms by the device manufacturer.
To increase their hardware market adoption, semiconductor companies are investing heavily in the software development toolkits integrated with ML/DL frameworks to deliver as a package that allows developers to quickly get started with all components for embedded systems development at ease. This allows the device manufacturer to effectively utilize the silicon resources in a shorter time-span and gain an advantage by being first to market. Edge AI chipset market is witnessing software and associated technology stacks facilitating wider adoption and faster development cycle.
AI processing at the Edge has allowed semiconductor companies and electronic device manufacturers to look beyond the horizon and re-define themselves with innovative solutions. It would be safe to say that Edge AI is driving the digital transformation and guarantees immense potential.”
The interesting aspect of all the assessments of Ai at the Edge is that every potential difficulty that commentators and industry point out Peter van der Made, Anil Mankar and the amazing team at Brainchip past and present have addressed in the AKIDA technology solution.
It is almost as if the Brainchip team had made a decision to identify every possible requirement and difficulty that could ever arise at the edge and then designed on the basis that AKIDA was going to conquer every single one. Ludicrously low power, no need for cooling, ultra high performance, capacity to use all existing networks and convert same to SNN on chip, incremental and on chip learning, unconnected autonomous processing when required, intelligent connection to address bandwidth issues, cyber secure, sensor agnostic, incredible breadth of scalability, digital, chip or IP, training via Tensorflow.
My opinion only DYOR
FF
AKIDA BALLISTA
