tag: Expedera

Why Vision LLMs Force A Rethink Of Edge AI Hardware
By Athish Rao - 14 May, 2026 - Comments: 0

As vision-centric large language models move on-device, performance measured in raw TOPS is no longer enough. Architectures need to be built around real workloads, memory behavior, and sustained utilization, especially at the edge. Vision LLMs are changing the edge AI equation For the last decade, most edge AI silicon has been built to do one job extremely well: run convolutional networks for... » read more

Chiplets Need A New Workflow
By Ann Mutschler - 14 May, 2026 - Comments: 0

Key Takeaways: Chiplet design turns semiconductor development into a system-level problem, requiring coordinated workflows across design, packaging, verification, test, and reliability. Successful chiplet workflows must handle multi-physics challenges — especially thermal, mechanical, power, and signal integrity — early enough to reduce costly failures before assembly and tape-out. ... » read more

Flash Getting Stacked High-Bandwidth Version
By Bryon Moyer - 14 May, 2026 - Comments: 1

Key takeaways: A new HBF 3D flash stack is similar to HBM for use in AI processing. HBF capacity will be much higher, allowing static storage of AI model weights, with optimized read speed. Samples are due out later this year, with accelerators featuring it coming out next year. AI inference using modern models requires billions of parameters, and moving them to where they c... » read more

Designing Chips In The Context Of Rapidly Evolving AI
By Ann Mutschler - 04 May, 2026 - Comments: 0

Key Takeaways: Agentic edge AI drives long-lived, tool-mediated loops with variable demands for compute, tokens, and memory. Edge PPA is dominated by memory hierarchy and data movement, forcing tight feature triage and robust RAS. Rapid model churn (multimodal, MoE, new formats) requires programmable, headroom-rich compute, interconnect, and runtime. Experts At The Table: Ch... » read more

Can Edge AI Keep Up?
By Ann Mutschler - 23 Apr, 2026 - Comments: 0

Key Takeaways: Model development is outpacing silicon design cycles, so edge AI architectures must prioritize adaptability. The required cadence for model updates is highly application-dependent and is closely tied to product lifetime and operational risk. Adaptability can conflict with power, performance, and area targets, so effective heterogeneous architectures and robust softwa... » read more

The Coming Breakup Between AI And The Cloud
By Sharad Chole - 09 Apr, 2026 - Comments: 0

For a decade, cloud AI has felt inevitable. It powers our voice assistants, photo libraries, recommendation engines, and a growing list of “smart” features we barely notice anymore. Yet beneath the convenience is a fragile dependency: if your connection stutters, your intelligence does too.​ We rarely question this arrangement, but we should. As models grow larger and expectations grow... » read more

Fast Isn’t Fast Enough: Redefining Metrics for Edge AI
By Ann Mutschler - 09 Apr, 2026 - Comments: 0

Key Takeaways: Edge AI performance is about low latency and power efficiency, not peak TOPS. Memory bandwidth and data movement now limit edge AI more than compute. Successful edge AI requires balanced hardware, software, and fast model updates. Experts At The Table: Today’s chip architect must contend with multiple factors when architecting AI processors for fast and effi... » read more

AI At The Edge Ubiquitous, Agentic, Multimodal, and Hardware-Accelerated
By Expedera - 08 Apr, 2026 - Comments: 0

Over the past decade, cloud-based artificial intelligence (AI) has undergone significant maturation. Cloud-based AI now reliably supports large-scale model training, massive data storage, and centralized orchestration of AI workloads. At the same time, limitations—such as latency, bandwidth costs, privacy concerns, catastrophic consequences in the event of failure, and dependency on continuou... » read more

AI Power on the Edge
By Brian Bailey - 12 Mar, 2026 - Comments: 0

Key takeaways Power and thermal become primary design considerations, not just optimizations. Hardware architectures need to be developed from the ground up. Hardware/software/model co-development is essential. Implementing AI on the edge is driven by a different set of metrics than training or even inference in the cloud. It makes power a first-class citizen, if not the mos... » read more

Security in Data Centers for AI Applications
By Expedera - 11 Mar, 2026 - Comments: 0

AI data centers are the engines of the new data revolution, transforming data lakes and extracting meaningful insights guided by user queries. In this white paper, we revisit the security problem and highlight that AI data centers pose specific risks whose impact extends far beyond initial expectations. Starting from the premise that the AI is “only as good as the data that comes in/out”, w... » read more

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