Ashish Ahuja reflects on what makes embedded developer kits genuinely useful: not flashy demos, but repeatable scaffolding for proof-of-concept work—board standards, connectors, reference designs, example projects, and an ecosystem path that shortens time-to-first-blink. He previously led a global initiative to define and productize a family of innovation kits around the Renesas RA microcontroller line, and he shares how that kind of “kit strategy” affects adoption and engineering throughput. https://about.me/ashishahuja
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His story tracks the industry’s jump in abstraction, from an 8051/Atmel-era board programmed in assembly to electrical engineering, Texas Instruments automotive, and then years spanning applications engineering, product marketing, product management, ecosystems, and business development. Based in Santa Clara in Silicon Valley, he frames embedded as a broad stack where silicon capability, firmware ergonomics, and developer experience have to align around a clear role.
The core trend he calls out is compute moving from cloud to node: keeping data local reduces exposure, removes round-trip latency, and avoids the power cost of constant uplink. That shift is pushing microcontroller-class designs toward mixed workloads (real-time control plus ML inference), using multicore partitions and dedicated accelerators like an NPU. He cites examples like Renesas RA8P1 (Arm Cortex-M85 with Helium MVE plus Ethos-U55 NPU, often positioned as a 1 GHz-class edge-AI MCU built on a 22 nm ultra-low-leakage process) and Infineon PSOC Edge parts that pair Cortex-M55 + Ethos-U55 alongside a Cortex-M33 for power-aware control in one chip.
He also zooms in on toolchain evolution: modern IDEs and configurators generate peripheral drivers, middleware wiring, and build artifacts, while LLM-assisted features can surface relevant code patterns without living inside datasheets. The constraints don’t disappear—determinism, memory maps, DMA/interrupt behavior, low-power modes, and security boundaries still rule—but the workflow is increasingly about validating and integrating what tooling generates into a reproducible build.
Filmed at Embedded World North America 2025 in Anaheim, the conversation ends on practical career advice: start with a €10–€15 kit, add sensor modules, and build a small project at home so debugging becomes normal. Between tutorials, vendor tech days, and AI copilots, it’s easier than ever to learn the “why” behind GPIO, timers, RTOS choices, and edge inference, then use internships or co-ops to turn that curiosity into a first product role early in your path.
Ashish Ahuja on Edge AI MCUs: Cortex-M85, Ethos-U55 NPU, dev kit strategy
Ashish Ahuja microcontroller career: from 8051 assembly to LLM-assisted embedded IDEs
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