Verification is a critical process for ensuring the correctness of modern processors. The increasing complexity of processor designs and the emergence of new instruction set architectures (ISAs) like RISC-V have created demands for more agile and efficient verification methodologies, particularly regarding verification efficiency and faster coverage convergence. While simulation-based approaches now attempt to incorporate advanced software testing techniques such as fuzzing to improve coverage, they face significant limitations when applied to processor verification, notably poor performance and inadequate test case quality. Hardware-accelerated solutions using FPGA or ASIC platforms have tried to address these issues, yet they struggle with challenges including host-FPGA communication overhead, inefficient test pattern generation, and suboptimal implementation of the entire multi-step verification process. In this paper, we present TurboFuzz, an end-to-end hardwareaccelerated verification framework that implements the entire Test Generation-Simulation-Coverage Feedback loop on a single FPGA for modern processor verification. TurboFuzz enhances test quality through optimized test case (seed) control flow, efficient inter-seed scheduling, and hybrid fuzzer integration, thereby improving coverage and execution efficiency. Additionally, it employs a feedback-driven generation mechanism to accelerate coverage convergence. Experimental results show that TurboFuzz achieves up to 2.23× more coverage collection than software-based fuzzers within the same time budget, and up to 571× performance speedup when detecting real-world issues, while maintaining full visibility and debugging capabilities with moderate area overhead.

The RISC-V instruction set architecture (ISA) offers flexibility for domain-specific custom instruction extensions. While the basic RISC-V ISA contains common instructions, the extended accelerators provide additional computing power to meet diverse needs. High-level synthesis (HLS) is often used to agilely create custom extension accelerators, allowing engineers to design complex digital circuits using high-level languages such as C/C++, further improving development efficiency. However, verifying a design that includes RISC-V cores and custom extensions is rarely studied and can be challenging. Traditional approaches for verifying HLS-generated designs use C-RTL co-simulation, primarily focusing on the unit level. This method can be extremely time-consuming and often makes impractical assumptions about interactions between HLS-generated circuits and the processor. Therefore, system-level verification is essential to extensively exercise the RISC-V cores, the custom extensions, and their interconnections.

High-Level Synthesis (HLS) enables software engineers to create intricate digital circuit designs using high-level languages like C/C++. While HLS tools can perform functional verification using C/C++ simulation, it is harder to verify that the generated RTL is also correct. This problem is exacerbated for designs which include HLS-generated IPs, such as PCIe or DDR interfaces, or hand-written RTL. While it is possible to perform cycle-accurate verification using C/RTL co-simulation, conventional methods are both slow and typically only focus on unit-level verification which can make it harder to identify the root cause of a bug.

Hardware verification is typically the bottleneck of the chip development cycle, mainly due to the time-consuming simulation and debugging process using software simulators. Assertion-Based Verification (ABV) has been widely adopted to provide better visibility into microarchitecture details and automatically detect unexpected behaviors. While ABV significantly improves verification efficiency, checking assertions using software simulators requires extremely long times for large benchmarks. Prototyping designs on an FPGA is a potential alternative to verify hardware, but it lacks fine-grained debugging capabilities for when errors occur.

Verification typically consumes the majority of the time in the hardware development cycle. Primarily this is because multiple iterations to debug hardware using software simulation is extremely time-consuming. While FPGAs can be utilised to accelerate the simulation, existing methods either provide limited visibility of design details, or are expensive to check against a reference model dynamically at the system level.

FPGA virtualization enables multiple tenants to share programmable hardware resources for application accelerations in cloud. However, such technique is still of limited usage in commercial FPGA cloud platforms, which mainly lies in: 1) absence of direct programming interfaces of the virtualized FPGA accelerators (vFPGAs) in tenants' virtual machines (VMs), 2) a fixed VM-vFPGA data movement scheme that is inadaptive to a wide range of data sizes among different applications, and 3) performance degradation due to unregulated inter-vFPGA competitions for limited shareable external resources (e.g., off-chip DRAM bandwidth). To tackle all the above issues, we propose a flexible FPGA virtualization framework and prototype an open cloud platform with ARM SoC-equipped FPGAs. Under such framework, tenants are allowed to directly initiate FPGA partial reconfiguration in isolated VMs via a direct I/O-like vFPGA device driver with as low as 20ms overhead. A hybrid data movement approach that leverages both memory-mapped I/O and DMA is also introduced in our framework to adaptively guarantee moderate VM-vFPGA bandwidth towards various data sizes. Moreover, a lightweight priority-based hardware scheduler is elaborated to monitor and dynamically allocate off-chip DRAM bandwidth among vFPGAs. Based on our preliminary infrastructure-level evaluation results, the proposed framework and the open prototyping are of significant interests to researchers looking forward to conducting further explorations in FPGA virtualization.

While research has shown that the agile chip design methodology is promising to sustain the scaling of computing performance in a more efficient way, it is still of limited usage in actual applications due to two major obstacles: 1) Lack of tool-chain and developing framework supporting agile chip design, especially for large-scale modern processors. 2) The conventional verification methods are less agile and become a major bottleneck of the entire process. To tackle both issues, we propose MINJIE, an open-source platform supporting agile processor development flow. MINJIE integrates a broad set of tools for logic design, functional verification, performance modelling, pre-silicon validation and debugging for better development efficiency of state-of-the-art processor designs. We demonstrate the usage and effectiveness of MINJIE by building two generations of an open-source superscalar out-of-order RISC-V processor code-named XIANGSHAN using agile methodologies. We quantify the performance of XIANGSHAN using SPEC CPU2006 benchmarks and demonstrate that XIANGSHAN achieves industry-competitive performance.

In this article, we describe an alternative circuit design methodology when considering trade-offs between accuracy, performance, and silicon area. We compare two different approaches that could trade accuracy for performance. One is the traditional approach where the precision used in the datapath is limited to meet a target latency. The other is a proposed new approach which simply allows the datapath to operate without timing closure. We demonstrate analytically and experimentally that on average our approach obtains either smaller errors or equivalent faster operating frequencies in comparison to the traditional approach. This is because the worst case caused by timing violations only happens rarely, while precision loss results in errors to most data. We also show that for basic arithmetic operations such as addition, applying our approach to the simple building block of ripple carry adders can achieve better accuracy or performance than using faster adder designs to achieve similar latency.

Digital circuits are currently designed to ensure timing closure. Releasing this constraint by allowing timing violations could lead to significant performance improvements, but conventional forms of computer arithmetic do not fail gracefully when pushed beyond deterministic operation. In this paper we take a fresh look at Online Arithmetic, originally proposed for digit serial operation, and synthesize unrolled digit parallel online operators to allow for graceful degradation. We quantify the impact of timing violation on key arithmetic primitives, and show that substantial performance benefits can be obtained in comparison to binary arithmetic. Since timing errors are caused by long carry chains, these result in errors in least significant digits with online arithmetic, causing less impact than conventional implementations. Using analytical models and empirical FPGA results from an image processing application, we demonstrate an error reduction over 89% and an improvement in SNR of over 20dB for the same clock rate.

Online arithmetic has been widely studied for ASIC implementation. Online components were originally designed to perform computations in digit serial with most significant digit (MSD) first, resulting in the ability to chain arithmetic operators together for low latency. More recently, research has shown that digit parallel online operators can fail more gracefully when operating beyond the deterministic clocking region in comparison to operators with conventional arithmetic. Unfortunately, the utilization of online arithmetic operators in the past has required a large area overhead for FPGA implementation. In this paper, we propose novel approaches to implement the key primitives of online arithmetic, adders and multipliers, efficiently on modern Xilinx FPGAs with 6-input LUTs and carry resources. We demonstrate experimentally that in comparison to a direct RTL synthesis, the proposed architectures achieve slice savings of over 67% and 69%, and speed-ups of over 1.2x and 1.5x for adders and multipliers, respectively. As a result, the area overheads of using online adders and multipliers in place of traditional arithmetic primitives is reduced from 8.41 x and 8.11 x to 1.88x and 1.84x respectively. Finally, because an online multiplier generates MSDs first, we also demonstrate the method to create an online multiplier with a reduced precision output that is smaller than a traditional multiplier producing the same result. We show that this can lead to silicon area savings of up to 56%.

Power switches are used as a part of the power-gating technique to reduce the leakage power of a design. To the best of our knowledge, this is the first report in open literature to show a systematic diagnosis method for accurately diagnosing power switches. The proposed diagnosis method utilizes the recently proposed design-for-test solution for efficient testing of power switches in the presence of process, voltage, and temperature variation. It divides power switches into segments such that any faulty power switch is detectable, thereby achieving high diagnosis accuracy. The proposed diagnosis method is validated through SPICE simulation using a number of ISCAS benchmarks synthesized with a 90-nm gate library. Simulation results show that, when considering the influence of process variation, the worst case loss of accuracy is less than 4.5%; it is less than 12% when considering VT variations.

Embedded applications can often demand stringent latency requirements. While high degrees of parallelism within custom FPGA-based accelerators may help to some extent, it may also be necessary to limit the precision used in the datapath to boost the operating frequency of the implementation. However, by reducing the precision, the engineer introduces quantization error into the design. In this paper, we demonstrate that for many applications it would be preferable to simply overclock the design and accept that timing violations may arise. Since the errors introduced by timing violations occur rarely, they will cause less noise than quantization errors. Through the use of analytical models and empirical results on a Xilinx Virtex-6 FPGA, we show that a geometric mean reduction of 67.9% to 98.8% in error expectation or a geometric mean improvement of 3.1% to 27.6% in operating frequency can be obtained using this alternative design methodology.

Relaxing constraints of 100% accuracy in datapath can provide the freedom to create designs with better performance or energy efficiency. This paper develops probabilistic models, which enable us to explore these trade-offs for key arithmetic primitives. We show that because specific input patterns are required to cause timing violations and that these patterns arise rarely, a lower expected error can be attained by allowing some timing variations to occur, instead of reducing the precision of a circuit to meet a target latency. Experiments show that a mean reduction of 5.6× ~ 36.7× in error expectation and an improvement of 7.2dB ~ 19.7dB in signal-to-noise ratio can be obtained for practical applications.