tag: Quadric

Vision-Language-Action Models Arrive
By Mike Leonard - 14 May, 2026 - Comments: 0

The AI model type capturing the most attention across robotics and autonomous vehicles right now is the vision-language-action model, or VLA. At embedded AI conferences this year, particularly the recently held Embedded Vision Summit, VLAs were a main topic of discussion – not as a research curiosity, but as the architecture that teams building autonomous systems are actively targeting. If yo... » 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

Chip Industry Week In Review
By The SE Staff - 17 Apr, 2026 - Comments: 0

Acquisitions and business pivots Teradyne acquired Israel-based TestInsight, a semiconductor test provider with pattern conversion, validation, and virtual test capabilities. Credo plans to acquire DustPhotonics, a developer of silicon photonics PICs for optical transceivers. Molex plans to acquire Teramount, a provider of detachable, passive-alignment fiber-to-chip connectivity solu... » read more

Heterogeneous NPU Data Movement: What The Execution Flow Shows
By Steve Roddy - 14 Apr, 2026 - Comments: 0

Heterogeneous NPU designs bring together multiple specialized compute engines to support the range of operators required by modern AI models. This approach enables coverage across diverse workloads, but it also introduces a structural consequence: intermediate data must move between those engines. That movement consumes power, adds latency, and requires additional silicon resources, with effect... » read more

Startup Funding: Q1 2026
By Jesse Allen - 13 Apr, 2026 - Comments: 0

The new year started off with a bang for private semiconductor companies, with 18 garnering mega funding rounds exceeding $100 million, and two, Rapidus and Cerebras, reaching the $1 billion mark. Predictably, the vast majority of those are either designing chips primarily for AI inference workloads or attempting to overcome bandwidth limitations by improving interconnects from the chip level t... » read more

A New Era For Co-Processing
By Brian Bailey - 09 Apr, 2026 - Comments: 0

Key Takeaways: There is no single processor capable of executing everything efficiently, meaning that multiple processors are required. Maximum efficiency is gained by minimizing the movement of data. Architects must maximize efficiency for today's workloads, while also adding enough flexibility to handle tomorrow's. New processor architectures are rapidly evolving thanks to... » 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

State Of The Market For Edge Silicon
By Ed Sperling - 08 Apr, 2026 - Comments: 0

The explosion of data and the rapid ramp of AI is causing significant changes in how chips are architected. At the edge, the key metrics are power, latency, and performance, but those can vary significantly by application and by workload. Steve Roddy, chief marketing officer at Quadric, talks about the need to balance performance and efficiency with flexibility for different applications, what ... » read more

Memory Wall Gets Higher
By Brian Bailey - 26 Mar, 2026 - Comments: 1

Key Takeaways An increasing percentage of the chip area is consumed by the same amount of SRAM for each node shrink. The problem is not limited to leading-edge AI, as it will eventually impact even small MCUs and MPUs. Architectural changes may be required. Stacking SRAM chiplets on logic is possible but expensive. SRAM is a vital piece of all computing systems, but its fail... » read more

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