NVIDIA acquires ARM: Creating a next-generation AI platform

NVIDIA announced Sept. 14 an agreement to acquire ARM holdings from SoftBank for $40 billion, subject to regulatory approval in the U.S., the U.K., the European Union and China. The acquisition has been rumored for several weeks, but the announcement generated negative comments from ARM customers. The two companies’ IP portfolios complement each other, especially in the context of rapidly growing AI workloads. TBR believes the combined company can successfully create new integrated AI hardware platforms, while growing profitable in each former company’s primary business, graphics processors for NVIDIA and mobile CPUs for ARM.

Complementary IP and different business models

ARM is in the CPU business. NVIDIA is in the graphics processing unit (GPU) business, and NVIDIA GPUs are increasingly used in non-graphics AI processing applications. Both companies rely on microprocessor design to deliver value and grow their businesses, but the way each company monetizes its IP is very different. NVIDIA is a traditional product-based business; it makes processors and boards that it sells to equipment manufacturers and to cloud service providers. ARM follows a licensing model; it sells the rights to use its designs and instruction sets to equipment manufacturers that often modify the ARM designs to meet their needs.

One concern of current ARM customers is that NVIDIA will eventually move ARM to a product model; only NVIDIA will make hardware that incorporates ARM designs, shutting off customers’ ability to customize ARM-based chips. This would be a disaster for the major mobile OEMS, including industry behemoths Apple and Samsung. ARM chips power virtually all smartphones and tablets, and mobile vendors rely on derivative ARM designs for differentiated products. Apple makes its own modifications and recently announced that its PCs will be migrated from Intel to ARM processors, allowing the company to have a uniform hardware platform for all its major products. Samsung designs its own ARM processors but relies on third-party ARM designer Qualcomm for many of its products. To make matters more confusing, Samsung manufactures both Qualcomm and Apple processors.

NVIDIA announced that it would continue the current ARM licensing business model and, in fact, would license some of its GPU IP in the same manner. Nevertheless, ARM customers are concerned because strategically vital licensed IP would now be owned by a hardware vendor. TBR believes the ARM licensing model will continue for ARM designs and the same model will greatly benefit NVIDIA’s GPU business as well.

NVIDIA is transitioning from graphics to AI

NVIDIA is the dominant vendor in GPUs, and for that reason, if its processors were used only for graphics, its growth would be limited to the growth of graphics applications. GPUs, however, are also well-suited for AI deep learning applications because both graphics and deep learning rely on massively parallel processing.

2Q20 is a crossover quarter. For the first time, NVIDIA data center revenue, which is almost all AI, was greater than revenue from graphics applications in PCs. NVIDIA data center revenue grew 167% year-to-year; NVIDIA will soon be dominated by AI applications in data centers. There is competition in AI processors from Google’s tensor processing unit (TPU) and from field-programmable gate arrays (FPGAs), as well as several new AI processing entrants, including two from Intel. Nevertheless, NVIDIA enjoys an enormous lead in a very rapidly growing business.

GPUs and CPUs working together

GPUs and CPUs coexist. Every device that uses GPUs for AI needs CPUs for all the other required processing. In data centers, the CPU is now almost always an Intel product. While ARM designs are increasingly powerful, as illustrated by Apple’s decision to use them for PCs, they are not yet used widely for data center devices. Where the GPU is doing most of the work, however, ARM-NVIDIA designs could be quite viable. ARM-NVIDIA designs would also work well in edge devices. This synergy positions NVIDIA well in a world where deep learning is becoming increasingly important.

Applications for deep learning are becoming more diverse, creating a variety of settings and requirements for CPU-GPU platforms. This proliferation of design requirements is a challenge for a product-based company like NVIDIA. The ARM licensing business model fits this diversifying market very well. TBR believes NVIDIA will first experiment with the licensing of older GPU designs, but then move rapidly to licensing GPU IP for all AI applications, greatly accelerating adoption of NVIDIA designs for AI and inhibiting growth of competing AI chip designs.

The ARM acquisition will accelerate AI

While NVIDIA and ARM are not competitors, therefore reducing anti-trust concerns, many parties have expressed concerns about this acquisition. Both companies are very important, with NVIDIA dominating AI processors and ARM monopolizing mobile CPUs. There are also concerns about a U.S. company controlling these two critical components. In the U.K., there is concern about the loss of jobs. TBR, however, believes this union will prove beneficial, certainly to the combined company, but also to other companies basing their business on the growth of AI.