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Great article and Brainchip even manages a mention or two:Very good article worth a download and read. PDF is 26p and was too big to paste in a post or go to site for read.
Covers the industry and applications well imo, with various resource links and explains a lot of the intricacies and some of the tech jargon.
We get our mention towards bottom of article in the Edge and SNN categories.
Steve Blank Artificial Intelligence and Machine Learning– Explained
Artificial Intelligence is a once-in-a lifetime commercial and defense game changer (download a PDF of this article here) Hundreds of billions in public and private capital is being invested in Art…steveblank.com
Artificial Intelligence and Machine Learning– Explained
steve blank
2 days ago
Artificial Intelligence is a once-in-a lifetime commercial and defense game changer
(download a PDF of this article here)
Hundreds of billions in public and private capital is being invested in Artificial Intelligence (AI) and Machine Learning companies. The number of patents filed in 2021 is more than 30 times higher than in 2015 as companies and countries across the world have realized that AI and Machine Learning will be a major disruptor and potentially change the balance of military power.
Until recently, the hype exceeded reality. Today, however, advances in AI in several important areas (here, here, here, here and here) equal and even surpass human capabilities.
If you haven’t paid attention, now’s the time.
Artificial Intelligence and the Department of Defense (DoD)
The Department of Defense has thought that Artificial Intelligence is such a foundational set of technologies that they started a dedicated organization- the JAIC – to enable and implement artificial intelligence across the Department. They provide the infrastructure, tools, and technical expertise for DoD users to successfully build and deploy their AI-accelerated projects.
Some specific defense related AI applications are listed later in this document.
We’re in the Middle of a Revolution
Imagine it’s 1950, and you’re a visitor who traveled back in time from today. Your job is to explain the impact computers will have on business, defense and society to people who are using manual calculators and slide rules. You succeed in convincing one company and a government to adopt computers and learn to code much faster than their competitors /adversaries. And they figure out how they could digitally enable their business – supply chain, customer interactions, etc. Think about the competitive edge they’d have by today in business or as a nation. They’d steamroll everyone.
That’s where we are today with Artificial Intelligence and Machine Learning. These technologies will transform businesses and government agencies. Today, 100s of billions of dollars in private capital have been invested in 1,000s of AI startups. The U.S. Department of Defense has created a dedicated organization to ensure its deployment
Other AI Hardware Architectures
Spiking Neural Networks (SNN) is a completely different approach from Deep Neural Nets. A form of Neuromorphic computing it tries to emulate how a brain works. SNN neurons use simple counters and adders—no matrix multiply hardware is needed and power consumption is much lower. SNNs are good at unsupervised learning – e.g. detecting patterns in unlabeled data streams. Combined with their low power they’re a good fit for sensors at the edge. Examples: BrainChip, GrAI Matter, Innatera, Intel.
Analog Machine Learning AI chips use analog circuits to do the matrix multiplication in memory. The result is extremely low power AI for always-on sensors. Examples: Mythic (AMP,) Aspinity (AML100,) Tetramem.
Optical (Photonics) AI Computation promise performance gains over standard digital silicon, and some are nearing production. They use intersecting coherent light beams rather than switching transistors to perform matrix multiplies. Computation happens in picoseconds and requires only power for the laser. (Though off-chip digital transitions still limit power savings.) Examples: Lightmatter, Lightelligence, Luminous, Lighton.
AI Hardware for the Edge
As more AI moves to the edge, the Edge AI accelerator market is segmenting into high-end chips for camera-based systems and low-power chips for simple sensors. For example:
AI Chips in Autonomous vehicles, Augmented Reality and multicamera surveillance systems These inference engines require high performance. Examples: Nvidia (Orin,) AMD (Versal,) Qualcomm (Cloud AI 100,) and acquired Arriver for automotive software.
AI Chips in Cameras for facial recognition, surveillance. These inference chips require a balance of processing power with low power. Putting an AI chip in each camera reduces latency and bandwidth. Examples: Hailo-8, Ambarella CV5S, Quadric (Q16), (RealTek 3916N).
Ultralow-Power AI Chips Target IoT Sensors – IoT devices require very simple neural networks and can run for years on a single battery. Example applications: Presence detection, wakeword detection, gunshot detection… Examples: Syntiant (NDP,) Innatera, BrainChip
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