Start-up AlphaIC yesterday announced that it has begun sampling its Gluon coprocessor for edge AI inference to customers. AlphaIC claims the chip delivers competitive performance compared to Nvidia in vision workloads such as object detection. Gluon is based on AlphaIC's proprietary architecture that has an instruction set architecture (ISA) optimized for AI, and has been in development for two years. The ISA refers to the instructions that a chip can process. AlphaIC calls it Real AI Processor (RAT) architecture.

Everybody is taking a stab at designing artificial intelligence processors, or electronic chips that could become the brains of computers that act as if they were humans. The latest to tackle the task of designing AI chips is Vinod Dham, a former Intel executive known as the "father of the Pentium." He has teamed up with some younger chip designers to build RAP chips, or real AI processors. At AlphaICs, the team is creating a coprocessor chip that can do agent-based artificial intelligence. These RAP chips could one day be deployed in computing devices and autonomous cars to make decisions at lightning speeds, or in data centers on a massive scale.

A couple of weeks ago, this column wrote about a new hardware chip design for AI, and referenced a start-up firm called AlphaICs, which counts the renowned Vinod Dham among its founders. AlphaICs is trying to redefine the type of chip used for AI applications by designing a chip among a new class of processors called Tensor Processing Units (TPUs) that allow for several more pieces of data to be simultaneously processed on their chips. Hungry AI monster programmes need to crunch through enormous data stores in order to be able to continuously "learn", and the hope is that this new class of TPU chips, which are themselves an extension of GPUs, will be sufficient to handle the vast amount of data flying in from various devices that connect to the Internet. The realization that the war in AI is not just about the data, but also the ability to process it effectively through new hardware, has not been lost on the large tech giants. Microsoft, Amazon, Google and Facebook are huge buyers of hardware, and each has toyed with many start-ups such as AlphaICs to see whether a new class of chip would be required to handle AI tasks. Facebook has said in the past that it might try to design new types of chips for its own use.

Until now, most firms have been using the Graphical Processing Unit (GPU) architecture, originally developed for video games by firms such as Nvidia, to build out their Artificial Intelligence (AI) programmes. The GPU is much more capable of handling voluminous data than the humble Central Processing Unit (CPU) that is at the heart of most computers that you and I are familiar with. A couple of weeks ago, I wrote in this column about a new hardware chip design for AI, and referenced a start-up firm called AlphaICs, which counts the renowned Vinod Dham among its founders. AlphaICs is trying to redefine the type of chip used for AI applications by designing a chip among a new class of processors called Tensor Processing Units (TPUs) that allow for several more pieces of data to be simultaneously processed on their chips. Hungry AI monster programmes need to crunch through enormous data stores in order to be able to continuously "learn", and the hope is that this new class of TPU chips, which are themselves an extension of GPUs, will be sufficient to handle the vast amount of data flying in from various devices that connect to the Internet.

Most advances in Artificial Intelligence (AI) have so far been confined to software. Today's AI computer programmes are vast users of data. They sift through these data and use methods such as pattern recognition. For instance, an online retailer like Amazon looks at your past history of browsing for a particular product online and then "matches" this use pattern to effectively target advertisements to you through sites like Facebook and Google so that you are enticed to buy. This is simple enough, but a similar method sits behind more advanced uses of AI such as self-driving vehicles.