1,720,968 research outputs found
Balancing Accuracy and Energy Efficiency on Ultra-Low-Power Platforms for ECG Analysis
No full textThe widespread diffusion of long-term cardiac monitoring using wearable devices is a key opportunity for analyzing the health conditions of chronic patients. The continuous analysis of the heartbeat, even reduced to minimal configuration (i.e., two leads), can diagnose and keep track of many severe cardiac conditions, such as abnormal atrial and ventricular contractions. Since wearable devices are battery-powered, it is essential to design solutions that can improve the power efficiency of this monitoring, leveraging HW/SW optimization on low-power platforms. State-of-the-art algorithms based on advanced machine learning (ML) approaches achieve high accuracy but are extremely demanding in terms of energy consumption. In the context of battery-powered devices, determining a trade-off between accuracy and energy consumption is paramount to extending battery lifetime. This work presents a system design for analyzing the Electrocardiogram (ECG) signal to detect pathological conditions using an energy-efficient methodology based on Convolutional Neural Networks (CNNs). We assessed our solution on GAP9, a parallel microcontroller-class platform based on the RISC-V architecture. We achieved a 95.0% accuracy on the MIT-BIH Arrhythmia dataset, which includes five classes of pathological conditions. This value is marginally lower (3%) than the current state-of-the-art based on transformers. However, we identified the best energy-accuracy trade-off configuration, reducing the energy consumption of 3 x (0.03 mJ vs. 0.09 mJ) which guarantees a longer battery lifetime for critical applications
An application of semantic technologies to self adaptations
No full textIn this paper we present an approach to add self-adaptive features to software systems not initially designed to be self-adaptive. Rapid changes in users needs, available resources, and types of system faults are everyday concerns in operating complex systems. The ability to face these issues in a (semi-)automatic fashion is a welcome feature. MAPE-K (Monitor, Analyze, Plan, Execute - Knowledge), or one of its variations, is the basic architectural pattern around which most adaptation engines are built. The knowledge (K) element in that pattern is usually a collection of dynamic and static models representing relevant aspects of the system and its environment. Knowledge-based features can be encoded using various techniques and serve a number of disparate roles: providing dynamic views of the system (Reflection Models), representing reconfiguration policies (Evaluation Models), mapping reconfigurations into system-level adaptations (Execution Models), and so forth. In our approach all these models are unified by using ontologies and Semantic Web technologies; the resulting knowledge base is then used to drive adaptation activities. We discuss how the various MAPE-K components can be designed in order to take advantage of this knowledge base by applying our approach to a real-word case study: a deployed system that was not designed to perform automatic adaptation. We then discuss merits and limits of our proposal both in the context of this specific case study and in a broader scope
Optimizing BFloat16 Deployment of Tiny Transformers on Ultra-Low Power Extreme Edge SoCs
Full text linkTransformers have emerged as the central backbone architecture for modern generative AI. However, most ML applications targeting low-power, low-cost SoCs (TinyML apps) do not employ Transformers as these models are thought to be challenging to quantize and deploy on small devices. This work proposes a methodology to reduce Transformer dimensions with an extensive pruning search. We exploit the intrinsic redundancy of these models to fit them on resource-constrained devices with a well-controlled accuracy tradeoff. We then propose an optimized library to deploy the reduced models using BFLoat16 with no accuracy loss on Commercial Off-The-Shelf (COTS) RISC-V multi-core micro-controllers, enabling the execution of these models at the extreme edge, without the need for complex and accuracy-critical quantization schemes. Our solution achieves up to 220× speedup with respect to a naïve C port of the Multi-Head Self Attention PyTorch kernel: we reduced MobileBert and TinyViT memory footprint up to ∼94% and ∼57%, respectively, and we deployed a tinyLLAMA SLM on microcontroller, achieving a throughput of 1219 tokens/s with an average power of just 57 m
Accelerating Image-based Pest Detection on a Heterogeneous Multicore Microcontroller
No full textThe codling moth pest poses a significant threat to global crop production, with potential losses of up to 80% in apple orchards. Special camera-based sensor nodes are deployed in the field to record and transmit images of trapped insects to monitor the presence of the pest. This article investigates the embedding of computer vision algorithms in the sensor node using a novel state-of-the-art microcontroller unit (MCU), the GreenWaves Technologies' GAP9 system-on-chip, which combines 10 RISC-V general purposes cores with a convolution hardware accelerator. We compare the performance of a lightweight Viola–Jones detector algorithm with a convolutional neural network (CNN), MobileNetV3-SSDLite, trained for the pest detection task. On two datasets that differentiate for the distance between the camera sensor and the pest targets, the CNN generalizes better than the other method and achieves a detection accuracy between 83% and 72%. Thanks to the GAP9’s CNN accelerator, the CNN inference task takes only 147 ms to process a 320 × 240 pixel image. Compared to the GAP8 MCU, which only relies on general-purpose cores for processing, we achieved 9.5× faster inference speed. When running on a 1000 mAh battery at 3.7 V, the estimated lifetime is approximately 199 days, processing an image every 30 s. Our study demonstrates that the novel heterogeneous MCU can perform end-to-end CNN inference with an energy consumption of just 4.85 mJ, matching the efficiency of the simpler Viola–Jones algorithm and offering power consumption up to 15× lower than previous methods
Parallel Execution of the Viola-Jones Algorithm on MCUs for Low-Cost Automated Pest Detection
No full textBio-inspired Autonomous Exploration Policies with CNN-based Object Detection on Nano-drones
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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