tag: Mobile

Four Architectural Opportunities for LLM Inference Hardware (Google)
By Technical Paper Link - 09 Jan, 2026 - Comments: 0

A new technical paper titled "Challenges and Research Directions for Large Language Model Inference Hardware" was published by Google. Abstract "Large Language Model (LLM) inference is hard. The autoregressive Decode phase of the underlying Transformer model makes LLM inference fundamentally different from training. Exacerbated by recent AI trends, the primary challenges are memory and in... » read more

Predictable Design Optimization And Closure With Adaptive Scenario Compression
By James Chuang - 13 Nov, 2025 - Comments: 0

Modern semiconductor chip design faces growing complexity due to numerous timing scenarios driven by varying operating conditions and physical effects. This complexity is especially pronounced in mobile and automotive chips, which require optimization across diverse performance and reliability demands. Designers currently focus on a limited subset of scenarios to manage computational load, but ... » read more

LPDDR6: Not Just For Mobile Anymore
By Ed Sperling - 03 Nov, 2025 - Comments: 0

LPDDR memory has been almost synonymous with mobile devices, but starting with the new LPDDR6 specification released in July 2025 by JEDEC, it will begin showing up inside of data centers, as well, early next year. The key factors in various flavors of DRAM are bandwidth, capacity, and cost. HBM is the fastest, but it's also expensive, and it requires a 2.5D or 3.5D packaging approach. GDDR is ... » read more

LPDDR: A Versatile Memory Powering The Next Wave Of Mobile, Edge & Endpoint Computing
By Raj Uppala - 11 Sep, 2025 - Comments: 0

The world of computing is evolving at a breakneck pace. From smartphones and ultra-thin laptops to autonomous vehicles and edge AI devices, the demand for memory that balances performance, power efficiency, and compact form factors has never been greater. This shift is driven by a few undeniable trends, including the increased deployment of AI models across verticals at the edge and higher us... » read more

How Neural Super Sampling Works: Architecture, Training, And Inference
By Liam O'Neil - 11 Sep, 2025 - Comments: 0

This blog post is the second in our Neural Super Sampling (NSS) series. The post explores why we introduced NSS and explains its architecture, training, and inference components. In August 2025, we announced Arm neural technology that will ship in Arm GPUs in 2026. The first use case of the technology is Neural Super Sampling (NSS). NSS is a next-generation, AI-powered upscaling solution. ... » read more

How Fast A GPU Do You Need For Your User Interface?
By Eleanor Brash - 04 Sep, 2025 - Comments: 0

Graphical user interfaces (GUIs) are common across all walks of life, from your smartphones to your TVs and even in your cars. Over the past decade, their complexity has evolved, moving from a simple background with basic icons into beautiful device differentiators, with 3D elements and micro-interactions that enhance the experience: shifting the perception when a phone tilts, or providing an... » read more

Complex Mix Of Processors At The Edge
By Liz Allan - 18 Aug, 2025 - Comments: 1

With AI changing so fast, it’s a juggle for companies to ensure they can deliver the best performance now while also future-proofing for unknown AI models or a completely different approach to training and inference that may emerge. There are a slew of options for high-end and budget phones, hyperscalers, and low-cost, low-power edge devices, and while GPUs keep making headlines, many designe... » read more

Start Experimenting With Neural Super Sampling For Mobile Graphics
By Sergio Alapont Granero - 14 Aug, 2025 - Comments: 0

Mobile game developers around the world face increasing pressure to meet user expectations for sharper visuals, smoother gameplay, and longer battery life. Balancing these goals on constrained mobile devices often means making trade-offs. Traditional upscaling methods offer limited flexibility. Real-time AI rendering remains complex, power-hungry, or hardware dependent. Neural Super Sampling... » read more

MIPI in FPGAs for Mobile-Influenced Devices
By Mixel - 14 May, 2025 - Comments: 0

A new wave of applications for mobile-influenced devices, using technology initially designed for mobile devices, demand high-resolution, high-frame-rate streaming data from vision sensors, especially with the rise of AI inference models performing real-time scene and object classification. These applications include automotive, home automation displays, medical device displays, survei... » read more

Power Budgets Optimized By Managing Glitch Power
By Adam Kovac - 16 Jan, 2025 - Comments: 0

“Waste not, want not,” says the old adage, and in general, that’s good advice to live by. But in the realm of chip design, wasting power is a fact of physics. Glitch power – power that gets expended due to delays in gates and/or wires – can account for up to 40% of the power budget in advanced applications like data center servers. Even in less high-powered circuits, such as those fou... » read more

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