This talk will discuss how the X-HEEP platform enables a streamlined, open-source, and efficient development of new hardware components and new software flows that target such hardware, including AI deployment on heterogeneous accelerators. Moreover, the talk will discuss hardware and software methods to deal with safety and security aspects. In particular, the talk will focus on three main aspects: i) a near-memory hardware platform designed and tailored to artificial intelligence (AI) and machine learning (ML) applications, mainly deep neural networks (DNNs). This hardware is integrated into the X-HEEP ecosystem, demonstrating the crucial need for an open-source hardware environment to deploy new accelerators efficiently; II) a flexible and automated deployment framework that can be easily extended to new hardware for executing full-fledged DNNs on it. The pipeline will be demonstrated on previous existing open-source hardware, showing the key aspects to be adapted for new hardware platforms as the one shown in the first part; and III) methodologies and key figures to deal with the safety and security of RISC-V systems. Concerning safety, a brief overview of the methods to develop Software Test Libraries (STLs) will be shown, as well as fault grading experiments on RISC-V cores embedded in the X-HEEP ecosystem. On the other side, a preliminary analysis regarding the security issues of a tinyML application fully implemented on X-HEEP was done.

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