The success of Artificial Intelligence (AI) applications is driven by efficient hardware accelerators. Recent trends show a rapid increase in the application demands, which in most cases surpass the available resources in the accelerators. As such, the efficient management of these limited resources becomes a critical factor in achieving high-performance.

Deep Learning (DL) applications are entering every part of our life given their ability to solve complex problems. Nevertheless, energy efficiency is still a major concern due to the large computational and memory requirements. State-of-the-art accelerators strive to address this issue by optimizing the architecture to the compute requirements of DL algorithms. However, there is always a mismatch between compute and memory requirements and what is offered by a particular design. A way to close this gap is by providing run-time adaptation or resource allocation to improve efficiency.

The VEDLIoT project aims to develop energy-efficient Deep Learning methodologies for distributed Artificial Intelligence of Things (AIoT) applications. During our project, we propose a holistic approach that focuses on optimizing algorithms while addressing safety and security challenges inherent to AIoT systems. The foundation of this approach lies in a modular and scalable cognitive IoT hardware platform, which leverages microserver technology to enable users to configure the hardware to meet the requirements of a diverse array of applications. Heterogeneous computing is used to boost performance and energy efficiency. In addition, the full spectrum of hardware accelerators is integrated, providing specialized ASICs as well as FPGAs for reconfigurable computing. The project's contributions span across trusted computing, remote attestation, and secure execution environments, with the ultimate goal of facilitating the design and deployment of robust and efficient AIoT systems. The overall architecture is validated on use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. Ten additional use cases are integrated via an open call, broadening the range of application areas.

Deep Learning (DL) is developing at an extremely fast pace. The increased number of applications, optimizations and hardware devices available, results in a multi-dimensional design space where the best performance is achieved with a detailed analysis of the hardware-software co-design process. Furthermore, the high demands for memory and the off-chip latency cost result in the on-chip memory becoming critical for achieving high performance and efficiency. In this work, we propose RAINBOW, a tool to assist in the hardware-software co-design for DL accelerators’ on-chip memory. The purpose is to help the design and/or deployment of a DL model to a dedicated accelerator. RAINBOW generates different analyses results and feeds them to the optimizers. The result is a heterogeneous execution plan combining different approaches and techniques depending on the dynamic requirements and constraints. In our analysis, we concluded that given the opportunity, RAINBOW’S heterogeneous plans are able to reduce the DRAM accesses to approximately half when compared to homogeneous plans.

The VEDLIoT project targets the development of energy-efficient Deep Learning for distributed AIoT applications. A holistic approach is used to optimize algorithms while also dealing with safety and security challenges. The approach is based on a modular and scalable cognitive IoT hardware platform. Using modular microserver technology enables the user to configure the hardware to satisfy a wide range of applications. VEDLIoT offers a complete design flow for Next-Generation IoT devices required for collaboratively solving complex Deep Learning applications across distributed systems. The methods are tested on various use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. VEDLIoT is an H2020 EU project which started in November 2020. It is currently in an intermediate stage with the first results available.