tag: edge AI

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

Research Bits: Apr. 21
By Jesse Allen - 21 Apr, 2026 - Comments: 0

Compute-in-memory state space models Researchers from the University of Michigan mapped complex state space models directly onto a compute-in-memory architecture in an example of hardware-software co-design for edge AI. "Compute-in-memory systems offer very high energy efficiency and throughput, but they are rigid and not optimal for convolution and transformer networks. In this study, we s... » 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

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

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

Embedded World 2026: Bringing Edge AI Into The Real World
By Neeta Shenoy - 02 Apr, 2026 - Comments: 0

Embedded World 2026 made one thing clear: AI is no longer confined to the cloud—it’s moving decisively onto the device. Across our demos and conversations, a consistent theme emerged: intelligence is shifting closer to where data is created—into devices, environments, and the physical world. From smart homes to industrial systems and a wide range of emerging robotics applications, the ... » read more

Rethinking Voice AI At The Edge: A Practical Offline Pipeline
By Odin Shen - 12 Mar, 2026 - Comments: 0

Cloud-based AI dominates the headlines, but responsive and private interaction lies at the edge. This blog post shows how to build a fully offline, real-time voice assistant using the Arm-based NVIDIA DGX Spark platform. The system integrates open-source components such as faster-whisper and vLLM. It delivers low-latency, human-like dialogue without sending data outside the local environment. ... » 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

The On-Device LLM Revolution
By Steve Roddy - 20 Feb, 2026 - Comments: 0

The AI world is experiencing a fundamental shift. After years of cloud-centric inference dominated by massive data center GPUs, we're witnessing an accelerating migration of language models to edge devices. These are not the trillion-parameter behemoths that require server farms, but the "Goldilocks zone" models: 3B to 30B parameters — large enough to deliver genuinely useful AI capabilities,... » read more

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