The Model H1 will need hardware level AI processing.
The Privatone team is currently exploring advancements in hardware and how we can incorporate better AI features and tools into our launch product. If costs permit, there may be on board NPU-like chip that does basic processing to reduce the overhead on the host PC. AI-specific chips and silicon are evolving rapidly to power edge intelligence in IoT devices and accessories, with 2026 marking a breakthrough year for integrated NPUs and neuromorphic designs.
Edge AI Accelerators for Mainstream IoT
New IoT SoCs are incorporating lightweight Neural Processing Units (NPUs), vector extensions, and DSP-like AI cores to enable on-device tasks like anomaly detection, vision analytics, and audio intelligence. These chips target sensors, modules, and gateways, delivering 2–10 TOPS of performance at 2–6W, making AI feasible for battery-constrained accessories like wearables and smart home devices. Vendors are expanding these capabilities beyond high-end gateways into everyday IoT tiers, accelerating shipments of AI-enabled chipsets.
Chiplet and RISC-V Architectures
Modular chiplet-based designs and RISC-V cores will rise in IoT silicon, allowing mix-and-match of compute, memory, and accelerators across process nodes for cost efficiency and customization. This shift from monolithic chips enables faster iteration for accessories, with open standards fostering interoperable components from multiple suppliers. Expect broader adoption in wearables and industrial IoT, where flexibility trumps raw power.
Neuromorphic and Ultra-Low-Power Innovations
Innatera’s Pulsar neuromorphic processor, debuting at CES 2026, redefines edge AI for IoT with event-based spiking neural networks that slash power by processing only changes in data—ideal for always-on sensors in accessories. These chips enable intelligence at the sensor without draining batteries or sending raw data to the cloud, targeting OEMs for real devices like smart tags and fitness trackers. Combined with MCU-class accelerators for TinyML, they support local inference under 1mW.
High-Performance SoCs for Advanced Accessories
For demanding IoT like robotics hubs or AR glasses, edge SoCs will deliver 15–30+ TOPS at 5–15W, handling complex models with peripherals and ecosystem support. Carbon-aware designs and security-by-design (e.g., hardware root-of-trust) become standard, while AI-assisted chip design shortens development cycles. RISC-V and chiplets further democratize these for startups building connected accessories.
Security and Sustainability Focus
Upcoming silicon prioritizes secure-by-design features like Arm’s Memory Tagging Extension and confidential enclaves, essential for autonomous IoT agents in consumer devices. Carbon footprint optimization influences layouts, favoring efficient processes like TSMC 3nm for low-power accessories. Local production trends may boost supply chain resilience for volume IoT manufacturing.
Market and Adoption Trends
Shipments of AI chipsets for IoT are projected to grow from $7.5B in 2024 to $28B by 2033, driven by edge AI in wearables, smart homes, and industrial sensors. Lattice Semiconductor and Arm predict explosive growth in 2026 as small language models and on-device performance mature. For accessories, this means smarter, private, and responsive devices without cloud dependency.