Activities performed have focused on most of the main axes of the project. More specifically, they have addressed the development of: (i) the augmented silicon photonics platform, (ii) novel photonic devices based on phase-change materials (PCMs) and related modeling, (iii) architectures for computing and for security layers, (iv) novel security metrics and protocols, (v) a gem5-based simulation platform, and (vi) methodologies for use-cases benchmarking.
From a technological point of view, the GeSe PCM that was chosen after optical testing, has been integrated in the silicon photonics platform. The fabrication of the full process is still in progress.
To handle the multi-physics character of photonic devices based on PCMs, multiple functionalities have been added to the software under development to deal with statistical properties of PCM phase dynamics.
Then, novel photonic designs based on electrically-actuated PCMs have been developed to enable multi-level operation at ultra-low insertion losses.
The architecture of the computing platform encompassing the RISC-V processor and interfaces compatible with the photonic accelerator was finalized and an API has been developed in order to communicate between the RISC-V core and the photonic accelerator. Peripheral circuitry has also been developed in order to allow interfacing between the photonic PIC and the controlling FPGA.
Security protocols based on mutual authentication and remote software attestation leveraging the high speed of operation of PUFs have been refined as well as the investigation of the robustness of photonic PUFs in terms of temperature stability, noise, and ML attacks.
To model the power consumption, latency, and compute density of the accelerator and of its security layers, a dedicated gem5-based simulation platform has been further developed adding new functionalities as well as compatibility with photonic accelerators. The platform can handle co-integration of electronic and photonic systems with RISC-V interfaces.
Benchmarking methodologies for accurate modeling of the NEUROPULS accelerator have been refined.