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AI chips: Explosive growth of deep learning is leading to rapid evolution of diverse, dedicated processors

Artificial intelligence (AI) utilization has been accelerating rapidly for more than 10 years, as decreases in memory, storage and computation cost have made an increasing number of applications cost-effective. The technique of deep learning has emerged as the most useful. Large public websites such as Facebook (Nasdaq: FB) and Amazon (Nasdaq: AMZN), with enormous stores of data on user behavior and a clear benefit from influencing user behavior, were among the earliest adopters and continue to expand such techniques. Publicly visible applications include speech recognition, natural language processing and image recognition. Other high-value applications include network threat detection, credit fraud detection and pharmaceutical research.

Deep learning techniques are based on neural networks, inspired by animal brain structure. Neural networks perform successive computations on large amounts of data. Each iteration operates on the results of the prior computation, which is why the process is called “deep.” Deep learning relies on large amounts computation. In fact, deep learning techniques are well known; the recent growth is driven by decreasing costs of data acquisition, data transmission, data storage and computation. The new processors all aim to lower the cost of computation.

The new chips are less costly than CPUs for running deep learning workloads

Each computation is limited and tends to require relatively low precision, necessitating fewer bits than found in typical CPU operations. Deep learning computations are mostly tensor operations — predominantly matrix multiplication — and parallel tensor processing is the heart of many specialized AI chips. Traditional CPUs are relatively inefficient in carrying out this kind of processing. They cannot process many operations at the same time, and they deliver precision and capacity for complex computations that are not needed.

Nvidia (Nasdaq: NVDA) GPUs led the wave of new processors. In 2012, Google announced that its Google Brain deep learning project to recognize images of cats was powered by Nvidia GPUs, resulting in a hundredfold improvement in performance over conventional CPUs. With this kind of endorsement and with the widespread acceptance of the importance of deep learning, many companies, large and small, are following the money and investing in new types of processors. It is not certain that the GPU will be a long-term winner; successful applications of FPGAs and TPUs are plentiful.

Intel: Optimizing its scale advantage for Business of One flexibility

TBR perspective

Usually sound business execution of world-class engineering, coupled with world-class monolithic manufacturing, has made Intel a dominant force around which technology businesses have orbited for decades. Intel’s dominance has been baked in the PC and server form factors, while ever smaller price points and form factors have shifted end-customer purchase criteria from computational performance specifications to business outcomes and user experiences.

Intel’s success has broadly expanded IT to address business problems and reshape our personal lives. Intel’s revenue growth prospects have diminished as its innovation has continued to increase the capacity and shrink the form factors and unit cost of its products. Intel delivers mature components that are embedded in mature products. Nevertheless, Intel thrives. The company has made mistakes, though, such as failing to address the mobile market. Intel’s capital- and engineering-intensive business requires it place large bets on its vision of the future. Now, facing waves of innovation in artificial intelligence (AI), Internet of Things (IoT) and processor design, Intel is, in effect, rearchitecting the company to reduce its dependence on the CPU, and thereby expand its market.

The key to Intel’s new architecture is companywide integration. Intel has always had more products and technologies, including video, networking, storage and memory silicon, than CPUs. As silicon becomes more diversified and is embedded in an increasing number of devices, Intel aims to create, along with customers, a far larger variety of solutions, often at a much smaller scale than the company’s monolithic products. To capitalize on the company’s enormous intellectual property, Intel must break down silos within the company. This will result in products that will often benefit from breaking down silos in silicon by facilitating the integration of computation, storage and communications.

The cultural challenge Intel will face will be in orchestrating and timing the various development teams such that the innovation cycles come together in world-class packages of tightly coupled compute, storage and networking form factors to power the smallest of edge compute instances and the largest of the high-performance computing (HPC) instances. The necessary work of rearchitecting the sales and marketing organizations remains for the next CEO, who has not yet been named, but the task is far less daunting than coordinating development and manufacture.

The thread that will stitch together these instances in the multicloud, always-on world of compute will be software. Software made interoperable through a “pruning,” as Intel Chief Engineering Officer and Technology, Systems Architecture & Client Group President Murthy Renduchintala described it, of the existing assets and frameworks into a cogent set of frameworks and tool sets to power innovation and optimize these scaled designs for specific workloads powered by AI is fed by voice and video as much as they have been fed by human interaction through keyboards in the past.

 

Intel Analyst Summit: Intel (Nasdaq: INTC) hosted an analyst event for the first time in four years to outline its technology road maps through 2021 and to articulate the business and cultural changes it believes are necessary for it to capitalize on the growing business opportunity Moore’s Law economics has unleashed. The senior leadership team gave about 50 analysts very detailed and frank briefings under a nondisclosure agreement (NDA), with ample time for follow-up conversations throughout the event.