Accurate, low-latency spatial beamforming is a crucial component in emerging smart hearable devices, enabling speech enhancement while suppressing noise and interference. In this work we present an optimized methodology for real-time execution of a neural network–based minimum variance distortionless response (MVDR) beamformer on resource-constrained microcontroller units (MCUs). With mixed-precision quantization, beamforming weights can be estimated at an energy cost of 13.2 mJ, which, along a fixed-interval scheduling strategy enables end-to-end real-time operation under a 20 ms latency constraint while achieving a short-time objective intelligibility (STOI) score of 88.4. Efficiency is further improved by integrating a speech activity detection network, which bypasses speech enhancement during silence, resulting in a reduction in energy consumption to 0.62 mJ per execution with 98.5% accuracy. In realistic deployment conditions, we estimate a lifetime of 16 h on a 100 mAh battery.

With the rise of embodied foundation models (EFMs), most notably small language models (SLMs), adapting Transformers for the edge applications has become a very active field of research. However, achieving the end-to-end deployment of SLMs on the microcontroller (MCU)-class chips without high-bandwidth off-chip main memory access is still an open challenge. In this article, we demonstrate high efficiency end-to-end SLM deployment on a multicore RISC-V (RV32) MCU augmented with ML instruction extensions and a hardware neural processing unit (NPU). To automate the exploration of the constrained, multidimensional memory versus computation tradeoffs involved in the aggressive SLM deployment on the heterogeneous (multicore+NPU) resources, we introduce Deeploy, a novel deep neural network (DNN) compiler, which generates highly optimized C code requiring minimal runtime support. We demonstrate that Deeploy generates the end-to-end code for executing SLMs, fully exploiting the RV32 cores’ instruction extensions and the NPU. We achieve leading-edge energy and throughput of$490 \; \mu $J per token, at 340 token per second for an SLM trained on the TinyStories dataset, running for the first time on an MCU-class device without the external memory.

Nano-sized drones, with palm-sized form factor, are gaining relevance in the Internet-of-Things ecosystem. Achieving a high degree of autonomy for complex multi-objective missions (e.g., safe flight, exploration, object detection) is extremely challenging for the onboard chip-set due to tight size, payload (2unknown room in a 3 minutes flight. By combining the detection CNN and the exploration policy, we show an average detection rate of 90 % on six target objects in a never-seen-before environment.

Today's software systems increasingly work in changing environments, where rapid modifications in user needs, resource variabilities and system faults require remarkable administrative efforts. In order to mitigate the costs for governing these activities, software systems are expected to dynamically self-adapt. The problem of supporting auto-adaptation, which is complex activity in itself, is further exacerbated when applied to legacy systems which have not been developed for this purpose. In this paper we introduce a novel approach to self-adaptation based on the MAPE-K paradigm, where semantic models are used to provide an unified view of the heterogeneous elements composing these systems, and reasoning mechanisms are leveraged to drive adaptation strategies. We present the implementation of an adaptation engine based these concepts that uses ontologies and Semantic Web technologies, and discuss its application in a real world case study. From this experience, we offer recommendations for future research in this area.