According to a report from Commercial Times, IC design giant MediaTek is making its move into the AI accelerator sector. MediaTek CEO Rick Tsai emphasized that MediaTek aims to be the best partner for edge AI, focusing on advanced technologies and 3nm process to optimize the power consumption and efficiency of its SoCs (system-on-chips).

Tsai mentioned that MediaTek will unveil its Dimensity 9400 flagship series of chips in October, designed to perfectly support most large language models on the market. He expressed confidence in achieving a more than 50% year-over-year revenue increase for flagship devices.

For the ASIC (Application-Specific Integrated Circuit) market, MediaTek has confirmed its entry into the AI accelerator field and will integrate CPUs as needed. ASICs are expected to start the revenue contribution  in the second half of next year.

Additionally, the TAM (Total Addressable Market) for generative AI is still in its early stages. MediaTek focuses on providing leading interconnect capabilities, such as SerDes IP and Ethernet PHY.

Regarding AI technology in the smartphone sector, Rick Tsai believes that flagship smartphones are seeing an increase in ASP, and there is a gradual shift towards high-end smartphones in the Chinese market. He noted that Chinese brands are actively developing AI, particularly in model development, such as open-source models like LLaMA 3.

At MediaTek’s earnings call on July 31st, Rick Tsai noted that the company anticipates a return to normal seasonal patterns in the second half of the year. The outlook for the fourth quarter will largely depend on consumer product demand.

Regarding the outlook in the third quarter, MediaTek expects the revenue to be flattish, ranging from NTD 123.5 billion to NTD 132.4 billion, compared with NTD 127.27 billion last quarter. Gross margin is projected to slide to 45.5-48.5%, from 48.8%, down 3.6 percentage points quarter over quarter and up 1.3 percentage points year-over-year.

Aside from the boost expected from the Dimensity 9400 flagship release, the company’s fourth-quarter market demand is projected to be relatively moderate, which is why the annual outlook remains unchanged. MediaTek expects its full-year gross margin to be between 46% and 48%.

Regarding TSMC’s pricing adjustments, Rick Tsai remains unperturbed, noting that all industry peers face similar cost pressures. MediaTek aims to reflect cost increases through pricing, with a gross margin target of 47% for new products. Progress has also been made in 3nm and 2nm processes, and MediaTek has secured its capacity needs for 2025 with TSMC.

Moreover, MediaTek and NVIDIA are collaborating on automotive chips, with plans to launch their first chip in early 2025. Rick Tsai mentioned that details are yet to be disclosed, but significant advancements in the automotive sector are expected between 2027 and 2028.

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(Photo credit: MediaTek)

The excitement surrounding ChatGPT has sparked a new era in generative AI. This fresh technological whirlwind is revolutionizing everything, from cloud-based AI servers all the way down to edge-computing in smartphones.

Given that generative AI has enormous potential to foster new applications and boost user productivity, smartphones have unsurprisingly become a crucial vehicle for AI tech. Even though the computational power of an end device isn’t on par with the cloud, it has the double benefit of reducing the overall cost of computation and protecting user privacy. This is primarily why smartphone OEMs started using AI chips to explore and implement new features a few years ago.

However, Oppo’s recent decision to shut down its chip design company, Zheku, casted some doubts on the future of smartphone OEMs’ self-developed chips, bringing the smartphone AI chip market into focus.

Pressing Needs to Speed Up AI Chips Iterations

The industry’s current approach to running generative AI models on end devices involves two-pronged approaches: software efforts focus on reducing the size of the models to lessen the burden and energy consumption of chips, while the hardware side is all about increasing computational power and optimizing energy use through process shrinkage and architectural upgrades.

IC design houses, like Qualcomm with its Snapdragon8 Gen.2, are now hurrying to develop SoC products that are capable of running these generative AI base models.
Here’s the tricky part though: models are constantly evolving at a pace far exceeding the SoC development cycle – with updates like GPT occurring every six months. This gap between hardware iterations and new AI model advancements might only get wider, making the rapid expansion of computational requirements the major pain point that hardware solution providers need to address.

Top-tier OEMs pioneering Add-on AI Accelerators

It’s clear that in this race for AI computational power, the past reliance on SoCs is being challenged. Top-tier smartphone OEMs are no longer merely depending on standard products from SoC suppliers. Instead, they’re aggressively adopting AI accelerator chips to fill the computational gap.

The approaches of integrating and add-on AI accelerator were first seen in 2017:

• Integrated: This strategy is represented by Huawei’s Kirin970 and Apple’s A11 Bionic, which incorporated an AI engine within SoC.
• Add-on: Initially implemented by Google Pixel 2, which used a custom Pixel Visual Core chip alongside Snapdragon 835. It wasn’t until the 2021 Pixel 6 series, which introduced Google’s self-developed Tensor SoC, that the acceleration unit was directly integrated into the Tensor.

Clearly, OEMs with self-developing SoC+ capabilities usually embed their models into AI accelerators at the design stage. This hardware-software synergy supplies the required computing power for specific AI scenarios.

New Strategic Models on the Rise

For OEMs without self-development capabilities, the hefty cost of SoC development keeps them reliant on chip manufacturers’ SoC iterations. Yet, they’re also applying new strategies within the supply chain to keep pace with swift changes.

Here’s the interesting part – brands are leveraging simpler specialized chips to boost AI-enabled applications, making standalone ICs like ISPs(Image Signal Processors) pivotal for new features of photography and display. Meanwhile, we’re also seeing potential advancements in the field of productivity tools – from voice assistants to photo editing – where the implementation of small-scale ASICs is seriously being considered to fulfill computational demands.

From Xiaomi’s collaboration with Altek and Vivo’s joint effort with Novatek to develop ISPs, the future looks bright for ASIC development, opening up opportunities for small-scale IC design and IP service providers.

Responding to the trend, SoC leader MediaTek is embracing an open 5G architecture strategy for market expansion through licensing and custom services. However, there’s speculation about OEMs possibly replacing MediaTek’s standard IP with self-developed ones for deeper product differentiation.

Looking at this, it’s clear that the battle of AI chips continues with no winning strategy for speeding up smartphone AI chip product iteration.

Considering the substantial resources required for chip development and the saturation of the smartphone market, maintaining chip-related strategies adds a layer of uncertainty for OEMs.With Oppo’s move to discontinue its chip R&D, other brands like Vivo and Xiaomi are likely reconsidering their game plans. The future, therefore, warrants close watch.

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