The Alif Blog

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AI hardware Acceleration: Breaking Beyond Traditional 32-bit MCUs

AI Hardware Acceleration Goes Beyond the Traditional 32-bit MCU Resource Constraints The 32-bit MCU has until now been restricted to basic applications for AI such as simple keyword detection. Now Alif the Ensemble architecture promises to bring vision and advanced voice use cases within the scope of the MCU. Introduction AI at the edge promises

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Why AI acceleration hardware is the answer to the conventional MCU’s struggles with machine learning workloads

Plunged into the completely new application environment presented by artificial intelligence, the conventional CPU-based architecture encounters considerable difficulties. Many embedded system developers have experienced this when trying to implement any inferencing operations beyond the simplest AI applications, such as vibration monitoring or basic keyword detection.

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Implementing Artificial Intelligence on Battery-Operated IoT Devices with Arm Helium

Endpoint devices are becoming increasingly intelligent, with many new devices capable of running complex machine learning models and other AI applications. However, adding intelligence to endpoint devices can be challenging, especially when considering cost and power constraints. Arm Helium vector processing technology can help solve this.

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Faster Inferencing with Lower Power Consumption

Design engineers who work with microcontrollers tend to find that they quickly run up against the barriers of performance and power when they try to add AI functionality to a typical embedded control system. The levers to pull when facing performance constraints in a conventional control application are normally CPU frequency and memory: increasing the CPU core’s speed and addressing more memory should result in a predictable uplift in throughput, and reduction in latency.

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