CEA-Leti Develops Novel Architecture for Keyword-Spotting (KWS) In Always-On, Voice-Activated Edge-AI Systems

ISSCC 2024 Presentation Reports Accurate Speech Recognition At Power Consumption Below One Microwatt

GRENOBLE, France – February 22, 2024 – CEA-Leti has developed a keyword-spotting system that dramatically improves accuracy in always-on, voice-activated Edge-AI systems and that consumes less power in a far smaller silicon footprint than current technology.

Presented in a paper at ISSCC 2024 in San Francisco, the new architecture uses time-domain signal processing on oscillators locked by injection and is suitable for devices running on energy harvesters, which supply power below 0.5V. The paper, “0.4V 988nW Time-Domain Audio Feature Extraction for Keyword Spotting Using Injection-Locked Oscillators”, reports accurate speech recognition at power consumption below one microwatt.

It describes the first injection-locked, oscillator-based time-domain audio feature extraction (TD-FEx) demonstrating keyword spotting operating down to 0.4V, while achieving 91 percent accuracy on 10 words. TD-FEx information is not coded as a voltage but as a time delay of two clocks’ signals. In addition to being well suited for advanced nodes, its advantages are digital-like implementation with low-supply voltage and better noise immunity than current systems. CEA-Leti’s system demonstrated accurate speech recognition with power consumption below 1 µW.

Some analog-based audio feature extraction (FEx) units using multi-channel Gm-C bandpass filters can supply 10 times the power efficiency of digital FEx units in a comparable silicon area. “However, analog FEx circuits have not demonstrated KWS with more than four keywords,” the paper reports. “They also suffer from a large footprint, challenging technology migration and limited dynamic range at low supply voltage, while speech signals have inherently a high dynamic range.”

“Our system’s silicon area of 0.15mm² is at least 3.5 times smaller than prior art on the same process node of 65nm,” said Ali Mostafa, lead author of the paper. “With a power of 988nW, our system is nine times more power-and-area efficient than ring-oscillator-based TD-FEx.”

Applications beyond speech recognition for this system include predictive maintenance and health monitoring that require on-line frequency decomposition of the sensor data.

About CEA-Leti (France)

CEA-Leti, a technology research institute at CEA, is a global leader in miniaturization technologies enabling smart, energy-efficient and secure solutions for industry. Founded in 1967, CEA-Leti pioneers micro-& nanotechnologies, tailoring differentiating applicative solutions for global companies, SMEs and startups. CEA-Leti tackles critical challenges in healthcare, energy and digital migration. From sensors to data processing and computing solutions, CEA-Leti’s multidisciplinary teams deliver solid expertise, leveraging world-class pre-industrialization facilities. With a staff of more than 2,000 talents, a portfolio of 3,200 patents, 11,000 sq. meters of cleanroom space and a clear IP policy, the institute is based in Grenoble, France, and has offices in Silicon Valley, Brussels and Tokyo. CEA-Leti has launched 75 startups and is a member of the Carnot Institutes network. Follow us on www.leti-cea.com