Abstract
The relevance of heterogeneous architectures has significantly increased over the last decade due to stagnation of performance scaling. Concurrently, increased performance-energy tradeoff requirements driven by the growth of edge computing, with a large focus on Artificial Intelligence (AI) inference, further motivates efforts towards hardware customization. In this context, the open RISC-V Instruction Set Architecture (ISA) and its custom extension oriented paradigm are a relevant technology towards this specialization. However, customizing a processor is a lengthy process requiring Hardware Description Language (HDL) expertise. Furthermore, for validation and simulation purposes, implementing an Instruction Set Simulator (ISS) of the modified core may also be a necessity. This introduces the need for development of two unrelated codebases, increasing development time and effort. In this paper, we explore High-Level-Synthesis (HLS) to realize both the hardware and the respective simulator through a single codebase, which reduces design effort and simplifies specialization of a RISC-V through specification of custom instructions at high level. We present a C++ based design of a RISC-V core, and validate it as an ISS, as well as a hardware module synthesized for an AMD Zynq UltraScale+ Field Programmable Gate Array (FPGA) through HLS, which we integrated in a System-on-Chip (SoC), and functionally validated through a state-of-the-art set of unit tests. © 2025 IEEE.

Abstract
Telecommunications and computer vision solutions have evolved significantly in recent years, allowing a huge advance in the functionalities and applications offered. However, these two fields have been making their way as separate areas, not exploring the potential benefits of merging the innovations brought from each of them. In challenging environments, for example, combining radio sensing and computer vision can strongly contribute to solving problems such as those introduced by obstructions or limited lighting. Machine learning algorithms, able to fuse heterogeneous and multi-modal data, are also a key element for understanding and inferring additional knowledge from raw and low-level data, able to create a new abstracting level that can significantly enhance many applications. This paper introduces the CONVERGE vision-radio concept, a new paradigm that explores the benefits of integrating two fields of knowledge towards the vision of View-to-Communicate, Communicate-to-View. The main concepts behind this vision, including supporting use cases and the proposed architecture, are presented. CONVERGE introduces a set of tools integrating wireless communications and computer vision to create a novel experimental infrastructure that will provide open datasets to the scientific community of both experimental and simulated data, enabling new research addressing various 6 G verticals, including telecommunications, automotive, manufacturing, media, and health.

Abstract
The rapid proliferation of Edge AI applications demands efficient, low-power computing architectures tailored to specific workloads. The RISC-V ecosystem is a promising solution, and has led to a fast growth of implementations based on custom instructions extensions, but with varying degrees of functionality and support which may hinder easy adoption. In this paper, we extensively review existing RISC-V extensions targeting primarily the AI domain and respective compilation flows, highlighting challenges in deployment, usability, and compatibility. We further implement and provide usable containerized environments for two of these works. To address the identified challenges, we then propose an approach for lightweight early validation of custom instructions via source-to-source transformations, without need of compiler modifications. We target our own Single Instruction Multiple Data (SIMD) accelerator, which we integrate into a CORE-V cv32e40px baseline core through custom instructions, and versus which we achieve up to 11.9x speedup for matrix-vector operations.

Abstract
Human Activity Recognition (HAR) is the identification and classification of static and dynamic human activities, which find applicability in domains like healthcare, entertainment, security, and cyber-physical systems. Traditional HAR approaches rely on wearable sensors, vision-based systems, or ambient sensing, each with inherent limitations such as privacy concerns or restricted sensing conditions. Instead, Radio Frequency (RF)-based HAR relies on the interaction of RF signals with people to infer activities. Reconfigurable Intelligent Surfaces (RISs) are significant for this use-case by allowing dynamic control over the wireless environment, enhancing the information extracted from RF signals. We present an Hand Gesture Recognition (HGR) approach using our own 6.5GHz RIS design, which we use to gather a dataset for HGR classification for three different hand gestures. By employing two Convolutional Neural Networks (CNNs) models trained on data gathered under random and optimized RIS configuration sequences, we achieved classification accuracies exceeding 90%.

Abstract
Wi-Fi 6E will enable dense communications with low latency and high throughput, meeting the demands of ever growing network traffic and supporting emergent services such as ultra HD or multi-video streaming, and augmented or virtual reality. However, the 6GHz band suffers from higher path loss and signal attenuation, and poor performance in NLoS conditions. Reconfigurable Intelligent Surfaces (RISs) can address these challenges by providing low-cost directional communications with increased spectral and energy efficiency. However, RIS designs for the WiFi-6E range are under-explored in literature. We present the implementation of an 8x8 RIS tuned for 6.5GHz designed for scalability. We characterize the response of the unit cell, and evaluate the RIS in an anechoic chamber, measuring the far field radiation patterns for several digital beamsteering configurations in a horizontal plane, demonstrating effective signal steering.

Abstract
Telecommunications and computer vision have evolved separately so far. Yet, with the shift to sub-terahertz (sub-THz) and terahertz (THz) radio communications, there is an opportunity to explore computer vision technologies together with radio communications, considering the dependency of both technologies on Line of Sight. The combination of radio sensing and computer vision can address challenges such as obstructions and poor lighting. Also, machine learning algorithms, capable of processing multimodal data, play a crucial role in deriving insights from raw and low-level sensing data, offering a new level of abstraction that can enhance various applications and use cases such as beamforming and terminal handovers. This paper introduces CONVERGE, a pioneering vision-radio paradigm that bridges this gap by leveraging Integrated Sensing and Communication (ISAC) to facilitate a dual View-to-Communicate, Communicate-to-View approach. CONVERGE offers tools that merge wireless communications and computer vision, establishing a novel Research Infrastructure (RI) that will be open to the scientific community and capable of providing open datasets. This new infrastructure will support future research in 6G and beyond concerning multiple verticals, such as telecommunications, automotive, manufacturing, media, and health.