Coarse Grain Reconfigurable Architectures (CGRAs) are an emerging hybrid computational architecture that has the parallel customization benefits of low-level logic devices such as FPGAs and ASICs, while the relative coarseness of these architectures makes CGRAs easier to design for, which is more similar to the traditional processor. In the process of mapping designs to CGRAs, flexible, fast, and adaptive Placement and Routing (P&R) is fundamental in order to implement efficient run-time reconfigurable frameworks. It is well-known that P&R is an NP-complete problem, and thus, solutions rely on heuristics to achieve quality results with acceptable execution times. CGRA P&R has different constraints compared to traditional VLSI P&R, e.g. path latency balancing and modulo scheduling of loops. In this work, we propose a graph-based P&R approach that uses graph traversals to map designs to CGRAs. Additionally, we parallelize our approach with a graph-based greedy heuristic that executes on a GPU. We compare our proposed P&R approach with the CGRA-ME framework, which implements Simulated Annealing and Integer Linear Programming placement algorithms. Our results show that this new approach can generate optimal mappings and improve the execution run-time up to several orders of magnitude. Furthermore, considering spatial mapping at the millisecond scale, our GPU approach is one order of magnitude faster compared to the state-of-the-art tool VPR.

In this work, we propose a framework called REconfigurable Accelerator DeploY (READY), the first framework to support polynomial runtime mapping of dataflow applications in high-performance CPU-FPGA platforms. READY introduces an efficient mapping with fine-grained multithreading onto an overlay architecture that hides the latency of a global interconnection network. In addition to our overlay architecture, we show how this system helps solve some of the challenges for FPGA cloud computing adoption in high-performance computing. The framework encapsulates dataflow descriptions by using a target independent, high-level API, and a dataflow model that allows for explicit spatial and temporal parallelism. READY directly maps the dataflow kernels onto the accelerator. Our tool is flexible and extensible and provides the infrastructure to explore different accelerator designs. We validate READY on the Intel Harp platform, and our experimental results show an average 2x execution runtime improvement when compared to an 8-thread multi-core processor.

Traditionally, inserting realistic Hardware Trojans (HTs) in complex hardware systems has been a time-consuming manual process, requiring comprehensive knowledge of the design and navigating intricate Hardware Description Language (HDL) codebases. Machine Learning (ML)-based approaches have attempted to automate this process but often struggle with the need for extensive training data, learning time, and limited generalizability across diverse hardware design landscapes. This paper introduces GHOST, an automated tool that leverages Large Language Models (LLMs) for rapid generation and insertion of HT. The research encompasses both the development of the GHOST framework and a comprehensive evaluation of its effectiveness across three state-of-the-art LLMs-GPT-4, Gemini-1.5-Pro, and Llama-3-70B. According to our evaluations, GPT-4 demonstrates the best performance by successfully generating and inserting HTs in 88.9% of its attempts. This study also highlights the security risks posed by LLM-generated HTs, as 100% of successful GHOST-generated HTs that completed inference within the time limit evaded detection by a state-of-the-art ML-based HT detection tool. These results underscore the need for advanced detection and prevention mechanisms in hardware security to address the emerging threat of LLM-generated HTs.

With the growing popularity of RISC-V and various open-source released RISC-V processors, it is now possible for computer engineers students to explore this simple and relevant architecture, and also, these students can explore and design a microcontroller at a low-level using real tool-flows and implement and test their hardware. In this work, we describe our experiences with undergraduate engineers building RISC-V architectures on an FPGA and then extending their experiences to implement an Arduino-like RISC-V tool-flow and the respective hardware and software to handle input-output ports, interrupts, hardware timers, and communication protocols. The microcontroller is implemented on an FPGA as a Senior Design project to test the viability of such efforts. In this work, we will explain how undergraduates can achieve these experiences including preparation for these projects, the tool-flows they use, the challenges in understanding and extending a RISC-V processor with microcontroller functionality, and a suggestion of how to integrate this learning into an existing curriculum, including a discussion on if we should include these deeper experiences in the Computer Engineering undergraduate curriculum.

Existing Hardware Trojan (HT) detection methods face several critical limitations: logic testing struggles with scalability and coverage for large designs, side-channel analysis requires golden reference chips, and formal verification methods suffer from state-space explosion. The emergence of Large Language Models (LLMs) offers a promising new direction for HT detection by leveraging their natural language understanding and reasoning capabilities. For the first time, this paper explores the potential of general-purpose LLMs in detecting various HTs inserted in Register Transfer Level (RTL) designs, including SRAM, AES, and UART modules. We propose a novel tool for this goal that systematically assesses state-of-the-art LLMs (GPT-4o, Gemini 1.5 Pro, and Llama 3.1) in detecting HTs without prior fine-tuning. To address potential training data bias, the tool implements perturbation techniques, i.e., variable name obfuscation, and design restructuring, that make the cases more sophisticated for the used LLMs. Our experimental evaluation demonstrates perfect HT detection rates by GPT-4o and Gemini 1.5 Pro in baseline scenarios (100%/100% precision/recall), with both models achieving better trigger line coverage (TLC: 0.82-0.98) than payload line coverage (PLC: 0.32-0.46). Under code perturbation, while Gemini 1.5 Pro maintains perfect detection performance (100%/100%), GPT-4o (100%/85.7%) and Llama 3.1 (66.7%/85.7%) show some degradation in detection rates, and all models experience decreased accuracy in localizing both triggers and payloads. This paper validates the potential of LLM approaches for hardware security applications, highlighting areas for future improvement.