PetaOps/W edge-AI μ Processors: Myth or reality?

With the rise of deep learning (DL), our world braces for artificial intelligence (AI) in every edge device, creating an urgent need for edge-AI SoCs. This SoC hardware needs to support high throughput, reliable and secure AI processing at ultra-low power (ULP), with a very short time to market. With its strong legacy in edge solutions and open...

Energy-efficient Edge Approximation for Connected Vehicular Services

Connected vehicular services depend heavily on communication as they frequently transmit data and AI models/weights within the vehicular ecosystem. Energy efficiency in vehicles is crucial to keep up with the fast-growing demand for vehicular data processing and communication. To tackle this rising challenge, we explore approximation and edge AI...

Dependability of Future Edge-AI Processors: Pandora’s Box

This paper addresses one of the directions of the HORIZON EU CONVOLVE project being dependability of smart edge processors based on computation-in-memory and emerging memristor devices such as RRAM. It discusses how how this alternative computing paradigm will change the way we used to do manufacturing test. In addition, it describes how these...

Trustworthy and Sustainable Edge AI: A Research Agenda

As a fast evolving domain that merges edge computing, data analytics and AI/ML, commonly referred as Edge AI, the community of Edge AI is establishing and gradually finds its way to connect with mainstream research communities of distributed systems, IoT, and embedded machine learning. Meanwhile, despite of its well-claimed potential to...

Design Patterns for Detecting and Mitigating Bias in Edge AI

From smart phones to speakers and watches, Edge Al is deployed on billions of devices to process large volumes of personal data efficiently, privately and in real-time. While Edge Al applications are promising, many recent incidents of bias in Al systems caution that Edge Al too, may systematically discriminate against groups of people based on...

Cooperative Task Execution for Object Detection in Edge Computing: An Internet of Things Application

The development of computer hardware and communications has brought with it many exciting applications in the Internet of Things. More and more Single Board Computers (SBC) with high performance and low power consumption are used to infer deep learning models at the edge of the network. In this article, we investigate a cooperative task...

Roadmap for edge AI: A Dagstuhl Perspective

Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI. In a nutshell, we envision Edge AI to provide adaptation for data-driven applications, enhance network and radio access, and allow the creation,...

Neural network partitioning for resource-limited environments

The demand for implementing neural networks on edge devices has rapidly increased as they allow designers to move away from expensive server-grade hardware. However, due to the limited resources available on edge devices, it is challenging to implement complex neural networks. This study selected the Kria SoM KV260 hardware platform due to its...

NanoFlowNet: Real-time optical flow estimation on a nano quadcopter

Nano quadcopters are small, agile, and cheap platforms well suited for deployment in narrow, cluttered environments. Due to their limited payload, nano quadcopters are highly constrained in processing power, rendering conventional vision-based methods for autonomous navigation incompatible. Recent machine learning developments promise high...

RRAM-based Low-Power Neuromorphic Computing Engine for Space Applications

With recent breakthroughs in AI and deep learning, applying these techniques to on-board computers for space applications has grown in interest to engineers on space applications. The space field brings its own challenges, such as reliability and power restrictions. The proposed solution in this work concerns a neuromorphic accelerator for a...

Object detection inside a wearable ultrasound neuromodulator patch: A deeper look into implementing ultrasound neuromodulator patches and how to find nerves using computer vision techniques

Neuromodulation of the vagus nerve is used as a treatment for all kinds of ailments and even as a means of improving the wearer's physiology, however, this form of treatment is not popular due to its invasive nature, high chance of side effects, and short period between reimplementation surgery, as such an alternative is sought in the form of...

Meta-learning for few-shot on-chip sequence classification

The growing interest in edge computing is driving the demand for more efficient deep learning models that fit into resource-constrained edge devices like Internet-of-Things (IoT) sensors. The challenging limitations of these devices in terms of size and power has given rise to the field of tinyML, focusing on enabling low-cost machine learning...

Towards Continuous Monitoring of Volatile Organic Compounds in the Gastrointestinal Tract

Much like wearable devices today, ingestible devices have emerged as a promising platform for continuous health monitoring, and potentially even intervention. Recent research has demonstrated the feasibility of ingestible devices with a retention mechanism, enabling them to remain in the stomach for weeks. Equipping these devices with sensors...

DNN-porting for maximizing inference hardware utilisation at the Edge

Industry 4.0 and the Industrial Internet of Things (IIoT) growth will result in an explosion of data generated by connected devices. Adapting 5G and 6G technology could be the leading enabler of the broad possibilities of connecting IIoT devices in masses. However, the edge solution has some disadvantages, such as the loss of resource elasticity...

Brain-inspired feature extraction for near sensor extreme edge processing with Spiking Neural Networks

Motivated by the desire to bring intelligent processing at the Edge, enabling online learning on resource- and latency-constrained embedded devices has become increasingly appealing, as it has the potential to tackle a wide range of challenges: on the one hand, it can deal with on-the-fly adaptation to fast sensor-generated streams of data under...