Energy-saving AI inference accelerator

Artificial Intelligence Researchers develop energy-efficient mixed-signal AI accelerator

2024-04-16 Source: Fraunhofer IIS | Translated by AI 1 min Reading Time

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AI has many advantages, but it requires computing power and thus energy. Researchers now want to minimize this resource consumption.

Adelia Gen 2 could be used in the future to detect atrial fibrillation via smartwatches, for example, or to perform AI-assisted sensor data classification locally at the sensor system.(Image: Shuo - stock.adobe.com) — Adelia Gen 2 could be used in the future to detect atrial fibrillation via smartwatches, for example, or to perform AI-assisted sensor data classification locally at the sensor system.
(Image: Shuo - stock.adobe.com)

The Fraunhofer Institute for Integrated Circuits IIS has developed one of the first mixed-signal inference accelerators. According to a statement, the AI inference accelerator chip "Adelia Gen 2" uses analog in-memory computing to process deep neural networks (DNNs) in a power-saving and energy-efficient manner. Thus, the chip can perform complex tasks with artificial intelligence (AI) energy-efficiently. This is achieved by combining the energy-efficient analog circuitry for calculating multiply-and-accumulate operations with the flexibility of digital systems in a mixed-signal system consisting of six NPUs (Neural Processing Units), the researchers said. This makes the chip one of the first of its kind that is both scalable and fully configurable.

Established systems typically still use purely digital inference accelerators as the basis for AI decision-making, according to the statement. However, these digital accelerators are comparatively energy-intensive and slower than their analog counterparts. By combining analog and digital computing, inference generation becomes ten times more efficient compared to purely digital accelerators.

Battery-powered systems, which require a long battery life, can particularly benefit from AI. Additionally, the latency times of Adelia are very low. According to the researchers, the chip can be used in various application fields, such as autonomous systems, Edge-AI, Smart Industry, Healthcare, Audio, Smart Sensors, Smart Wearables or Communication Systems. As a next step, the researchers want to run keyword spotting on Adelia Gen 2 to enable power-saving speech recognition.

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