DIgital-ANAlog (DIANA), a heterogeneous multi-core accelerator, combines a reduced
instruction set computer-five (RISC-V) host processor with an analog in-memory computing …

Extreme edge devices or Internet-of-Things (IoT) nodes require both ultra-low power (ULP)
always-on (AON) processing as well as the ability to do on-demand sampling and …

As a widely used speech-triggered interface, deep-learning-based keyword spotting (KWS)
chips require both ultra-low power and high detection accuracy. We propose a sub …

Keyword spotting (KWS) is a crucial function enabling the interaction with the many
ubiquitous smart devices in our surroundings, either activating them through wake-word or …

A fully integrated near-microphone keyword spotting (KWS) chip is proposed to directly
interact with a passive microphone and achieve submicrowatt power for the Internet of …

Keyword spotting (KWS) systems are used for human–machine communications in various
applications. In many cases, KWS involves a combination of wake-up-word (WUW) …

Tiny machine learning (TinyML) applications impose µJ/inference constraints, with a
maximum power consumption of tens of megawatt. It is extremely challenging to meet these …

Recurrent Neural Networks (RNNs) are useful in temporal sequence tasks. However,
training RNNs involves dense matrix multiplications which require hardware that can …

The current audio single-mode self-supervised classification mainly adopts a strategy based
on audio spectrum reconstruction. Overall, its self-supervised approach is relatively single …

Extreme edge devices or Internet of Thing nodes require energy-efficient, ultra-low power,
always-on processing, and the ability to do on-demand sampling and processing. Though …