Technology Insights

April 7, 2026

KWS on MCU and NPU: Achieving Sub-10 ms Inference at Low Power

Why KWS Is the Benchmark Edge-AI Workload on MCUs Key spotting (KWS) needs to hit two targets at the same time: fast reaction, and low energy. Alif Semiconductor® sees KWS show up again and again because it’s the smallest always-on edge AI workload that still exposes the real bottlenecks: audio capture, MFCC preprocessing, model operator

February 13, 2026

Modern 32-bit MCUs: A Buyer’s Framework for Power, Memory & Security

As the Internet-of-Things (IoT) and edge compute market evolves, embedded system designers face a growing checklist when selecting a 32-bit MCU. Today’s applications demand chips that deliver high-performance compute and memory while minimizing energy use, all with seamless connectivity and solid security. This guide walks through the key criteria engineers should look for in a modern MCU. You’ll learn why features like Autonomous Intelligent Power Management (aiPM®), on-chip SRAM/MRAM, built-in BLE radios, Arm ® TrustZone® secure enclaves, and integrated AI/DSP accelerators can make or break an MCU choice. secure enclaves, and integrated AI/DSP accelerators can make or break an MCU choice.

February 5, 2026

BLE MCUs: How to Choose for Battery Life, Audio & Mesh

Modern Bluetooth® Low Energy (BLE) integrates the radio on-chip with powerful Central Processing Unit (CPU) and Digital Signal Processor (DSP) cores, specialized low-power modes, and advanced power management. Bluetooth LE is the Bluetooth radio option designed for low power, short range wireless links in the 2.4 GHz band, typically optimized for small, intermittent data exchanges rather than continuous streaming. In these systems, battery life, audio capability, and mesh networking are the top selection criteria.

January 16, 2026

Edge AI on MCUs: The Practical Buyer’s Guide

Edge AI on microcontrollers (MCUs) has unleashed a new frontier for embedded AI MCU platforms, enabling ultra-low power devices to recognize voice, gestures, vibration patterns and more. This guide introduces key criteria and design choices that will assist in choosing the right MCU for your next edge AI project.The term edge AI encompasses a broad range of applications, where engineers must balance memory, power consumption, latency, footprint, toolchains and more to achieve robust AI inference on battery powered devices to match theirs. Below is a table of evaluation criteria for MCUs for edge AI applications.

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