Workload and latency
Define models, precision, concurrency, frame rate and worst-case response time. Use workload-level measurements instead of comparing headline TOPS alone.
Technology decision guide
Edge AI Compute: How to Choose a Product Route
Choose Edge AI compute from the workload and deployment constraints outward. Model accuracy or TOPS alone is not enough: sustained latency, memory movement, power, thermal design, software maturity, interfaces, security and production lifecycle determine whether a platform fits a real product.
Selection criteria
Compare the complete product route.
These criteria turn product claims into questions that can be verified with measurements, documentation and direct supplier evidence.
Memory and data movement
Check model size, bandwidth, camera or sensor ingest, preprocessing and whether memory pressure changes sustained performance.
Power and thermal envelope
Compare measured system power, throttling behaviour, cooling needs and performance inside the intended enclosure and ambient range.
Software path
Review compilers, model conversion, supported operators, BSP quality, debugging, update policy and the effort required to maintain a production image.
Interfaces and system fit
Validate cameras, sensors, storage, networking, real-time control and security requirements at the complete platform level.
Lifecycle and evidence
Confirm availability, revision policy, documentation, evaluation hardware and direct supplier evidence before design-in.
Qualification questions
Questions to answer before design-in.
Keep unresolved constraints visible until the exact SKU, software stack and operating context have been reviewed.
Which workload must run locally, and what is the actual latency ceiling?
What happens to performance under sustained thermal and memory pressure?
Which model operations require conversion, fallback or custom code?
Can the software and hardware lifecycle match the product's service life?
Which security, update and regional compliance questions remain unresolved?
Related Industry Signals
Current Taiwan capability evidence.
Signals summarize attributed primary-source announcements. Verify availability, exact specifications, lifecycle and commercial terms directly before procurement or design-in.
Sanctuary AI
Sanctuary AI Reports 99.5%+ Success on a Tier 1 Wire-Plugging Benchmark
On June 17, 2026, Sanctuary AI said its Physical AI system achieved a 99.5%+ task success rate and a 2.54-second cycle time on a wire-plugging task benchmarked against the live production requirements of an unnamed global Tier 1 automotive supplier. The commercial signal is not a humanoid launch. It is the company's shift toward putting learned manipulation on existing industrial robotic hardware, combined with first-party evidence that the automotive work has progressed beyond a laboratory demonstration. Sanctuary's current milestones page says the work resulted in a purchase order, although the scope, value and deployment volume remain undisclosed.
Read Signal →RealtekRealtek Pushes Edge AI Into Home Media, Radar Sensing and 100GbE
The strongest signal is breadth. Realtek is extending local AI from media enhancement into offline language-model interaction while also integrating radar-based human sensing with Wi-Fi 6, Bluetooth LE and Thread. At the infrastructure side, the company says its 100GbE solution is already in mass production, while a lower-power 10GbE generation remains a 2027 roadmap item. Product teams should evaluate each element on its own maturity curve.
Read Signal →MetaspectralMetaspectral Gets $2.55M to Scale AI Hyperspectral Sorting
On May 11, 2026, Pacific Economic Development Canada announced a CAD 2,554,685 investment in MLVX Technologies, which operates as Metaspectral. The government says the funding will support commercialization of Metaspectral's AI-powered imaging technology by enabling purchases of specialized sensors and hardware and by supporting skilled hiring. Metaspectral's current industrial-sorting offering, Clarity Recover, applies spectral analysis to classify difficult material streams in real time and is designed for low-latency edge deployment. The supply-chain signal is meaningful because the public funding announcement explicitly identifies hardware acquisition, but it stops well short of identifying sensor vendors, optical components, edge-compute platforms, quantities or purchase orders. For Taiwan suppliers, the relevant opportunity is therefore a capability-level intersection in hyperspectral sensing, optics, illumination, edge computing, industrial electronics and system integration rather than evidence of a current design win.
Read Signal →AAEONAAEON's New Micro-ATX Boards Define Two Edge AI Paths
The practical signal is not only that AAEON added two more Arrow Lake-S boards. It is that Micro-ATX is being positioned as a flexible edge-system base where CPU power, PCIe expansion, networking, displays and operating-system support can be chosen for different product tiers.
Read Signal →Product decision