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1. How Good Is Your Verilog RTL Code?: A Quick Answer from Machine Learning

   https://doi.org/10.1145/3508352.3549375  

   Sengupta, Prianka; Tyagi, Aakash; Chen, Yiran; Hu, Jiang (October 2022, 2022 IEEE/ACM International Conference On Computer Aided Design (ICCAD))

   Hardware Description Language (HDL) is a common entry point for designing digital circuits. Differences in HDL coding styles and design choices may lead to considerably different design quality and performance-power tradeoff. In general, the impact of HDL coding is not clear until logic synthesis or even layout is completed. However, running synthesis merely as a feedback for HDL code is computationally not economical especially in early design phases when the code needs to be frequently modified. Furthermore, in late stages of design convergence burdened with high-impact engineering change orders (ECO’s), design iterations become prohibitively expensive. To this end, we propose a machine learning approach to Verilog-based Register-Transfer Level (RTL) design assessment without going through the synthesis process. It would allow designers to quickly evaluate the performance-power tradeoff among different options of RTL designs. Experimental results show that our proposed technique achieves an average of 95% prediction accuracy in terms of post-placement analysis, and is 6 orders of magnitude faster than evaluation by running logic synthesis and placement. 

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2. PDL: a high-level hardware design language for pipelined processors

   https://doi.org/10.1145/3519939.3523455  

   Zagieboylo, Drew; Sherk, Charles; Suh, Gookwon Edward; Myers, Andrew C. (June 2022, ACM SIGPLAN Conf. on Programming Language Design and Implementation (PLDI))

   Processors are typically designed in Register Transfer Level (RTL) languages, which give designers low-level control over circuit structure and timing. To achieve good performance, processors are pipelined, with multiple instructions executing concurrently in different parts of the circuit. Thus even though processors implement a fundamentally sequential specification (the instruction set architecture), the implementation is highly concurrent. The interactions of multiple instructions---potentially speculative---can cause incorrect behavior. We present PDL, a novel hardware description language targeted at the construction of pipelined processors. PDL provides one instruction at a time semantics: the first language to enforce that the generated pipelined circuit has the same behavior as a sequential specification. This enforcement facilitates design-space exploration. Adding or removing pipeline stages, moving operations across stages, or otherwise changing pipeline structure normally requires careful analysis of bypass paths and stall logic; with PDL, this analysis is handled by the PDL compiler. At the same time, PDL still offers designers fine-grained control over performance-critical microarchitectural choices such as timing of operations, data forwarding, and speculation. We demonstrate PDL's expressive power and ease of design exploration by implementing several RISC-V cores with differing microarchitectures. Our results show that PDL does not impose significant performance or area overhead compared to a standard HDL. 

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3. Hardware Generation with High Flexibility using Reinforcement Learning Enhanced LLMs

   Zhao, Yifang; Fu, Weimin; Li, Shijie; Hu, Yi-Xiang; Guo, Xiaolong; Jin, Yier (November 2025, IEEE Design Automation Conference (DAC) 2025)

   The increasing complexity of integrated circuit design requires customizing Power, Performance, and Area (PPA) metrics according to different application demands. However, most engineers cannot anticipate requirements early in the design process, often discovering mismatches only after synthesis, necessitating iterative optimization or redesign. Some works have shown the promising capabilities of large language models (LLMs) in hardware design generation tasks, but they fail to tackle the PPA trade-off problem. In this work, we propose an LLM-based reinforcement learning framework, PPA-RTL, aiming to introduce LLMs as a cutting-edge automation tool by directly incorporating post-synthesis metrics PPA into the hardware design generation phase. We design PPA metrics as reward feedback to guide the model in producing designs aligned with specific optimization objectives across various scenarios. The experimental results demonstrate that PPA-RTL models, optimized for Power, Performance, Area, or their various combinations, significantly improve in achieving the desired trade-offs, making PPA-RTL applicable to a variety of application scenarios and project constraints. 

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4. RERTL: Finite State Transducer Logic Recovery at Register Transfer Level

   https://doi.org/10.1109/AsianHOST47458.2019.9006699  

   Portillo, Jason; Meade, Travis; Hacker, John; Zhang, Shaojie; Jin, Yier (December 2019, 2019 Asian Hardware Oriented Security and Trust Symposium (AsianHOST))

   Increasingly complex Intellectual Property (IP) design, coupled with shorter Time-To-Market (TTM), breeds flaws at various levels of the Integrated Circuit (IC) production. With access to IPs at all stages of production, design defects can easily be found and corrected, i.e., knowledge of the Register Transfer Level (RTL) code allows for the option of easy defect detection. However, third-party IPs are typically delivered as hard IPs or gate-level netlists, which complicates the defect detection process. The inaccessibility of source RTL code and the lack of RTL recovery tools make the task of finding high-level security flaws in logic intractable. Upon this request, in this paper, we present an RTL recovery tool suite named RERTL that leverages advanced graph algorithms including Lengauer-Tarjan's dominator tree and Euler tour tree technique to assist in netlist analysis. Supported by RERTL, logical states and their interactions are recovered from the initial design in the format of gate-level netlists. After the recovery of state interaction, RERTL further converts the full design into human-readable RTL. A series of netlist case studies were examined using RERTL covering benign logic structures, designs with accidental defects, and designs with deliberate backdoors. The experimental results show that all of our designs at various complexities were recoverable within seconds. 

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5. DigiQ: A Scalable Digital Controller for Quantum Computers Using SFQ Logic

   https://doi.org/10.1109/HPCA53966.2022.00037  

   Jokar, Mohammad Reza; Rines, Richard; Pasandi, Ghasem; Cong, Haolin; Holmes, Adam; Shi, Yunong; Pedram, Massoud; Chong, Frederic T. (April 2022, 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA))

   The control of cryogenic qubits in today’s super-conducting quantum computer prototypes presents significant scalability challenges due to the massive costs of generating/routing the analog control signals that need to be sent from a classical controller at room temperature to the quantum chip inside the dilution refrigerator. Thus, researchers in industry and academia have focused on designing in-fridge classical controllers in order to mitigate these challenges. Due to the maturity of CMOS logic, many industrial efforts (Microsoft, Intel) have focused on Cryo-CMOS as a near-term solution to design in-fridge classical controllers. Meanwhile, Supercon-ducting Single Flux Quantum (SFQ) is an alternative, less mature classical logic family proposed for large-scale in-fridge controllers. SFQ logic has the potential to maximize scalability thanks to its ultra-high speed and very low power consumption. However, architecture design for SFQ logic poses challenges due to its unconventional pulse-driven nature and lack of dense memory and logic. Thus, research at the architecture level is essential to guide architects to design SFQ-based classical controllers for large-scale quantum machines.In this paper, we present DigiQ, the first system-level design of a Noisy Intermediate Scale Quantum (NISQ)-friendly SFQ-based classical controller. We perform a design space exploration of SFQ-based controllers and co-design the quantum gate decompositions and SFQ-based implementation of those decompositions to find an optimal SFQ-friendly design point that trades area and power for latency and control while ensuring good quantum algorithmic performance. Our co-design results in a single instruction, multiple data (SIMD) controller architecture, which has high scalability, but imposes new challenges on the calibration of control pulses. We present software-level solutions to address these challenges, which if unaddressed would degrade quantum circuit fidelity given the imperfections of qubit hardware.To validate and characterize DigiQ, we first implement it using hardware description languages and synthesize it using state-of-the-art/validated SFQ synthesis tools. Our synthesis results show that DigiQ can operate within the tight power and area budget of dilution refrigerators at >42,000-qubit scales. Second, we confirm the effectiveness of DigiQ in running quantum algorithms by modeling the execution time and fidelity of a variety of NISQ applications. We hope that the promising results of this paper motivate experimentalists to further explore SFQ-based quantum controllers to realize large-scale quantum machines with maximized scalability. 

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