What if the only way to get real gains at the edge is to redesign everything—from the silicon atoms to the app you deploy? That’s the bet Professor-Founder Mohammed Ali made with EMAS, and the results are striking: continuous inference at milliwatts, microsecond wake/sleep cycles, and real benchmarks that hold up against the best in class while burning a fraction of the energy.

We walk through how a RISC-V core, dual AI accelerators, and an MRAM/RRAM-backed memory system work together to keep weights on-chip, slash data movement, and power-gate aggressively without losing state. The compiler handles pruning, quantization, and on-the-fly compression to achieve around 1.3 bits per weight without torpedoing accuracy, while a custom memory controller mitigates non-volatile quirks like endurance and read variability. Instead of chasing TOPS, the stack optimizes bandwidth, dataflow, and timing to match the realities of sensors and batteries.

The story gets especially interesting with drones. Since propellers—not processors—dominate energy use, EMAS applies tiny AI to the control problem, redistributing load across rotors in real time and extending flight endurance by 60% or more in hardware-in-the-loop simulations. We also dig into wearables and time-series workloads like ECG, audio, and vibration, where sparse sampling pairs perfectly with microsecond power gating. If you build at the edge, the dev experience matters: you’ll hear about the virtual dev kit with remote access to real silicon, a compact evaluation board with modular sensors, and an SDK that plugs into TensorFlow, PyTorch, and Zephyr. Advanced users can map trained models via a CLI; newcomers can lean on a NAS-based flow that proposes architectures meeting strict memory and power budgets.

If you care about edge AI, battery life, and shipping reliable products, this conversation is a blueprint for co-designing across the stack to unlock 10–200x energy gains without giving up performance. Subscribe, share with a teammate who owns your edge roadmap, and leave a review with the one use case you’d optimize first.

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From Lab to Low-Power: Building EMASS, a Tiny AI Chip That Runs on Milliwatts