This program is tentative and subject to change.

Wed 17 Jun 2026 15:00 - 15:20 at Flatirons 4 - Static Analysis 1

Static analysis has profoundly improved software quality over the past decades, evolving from compiler-integrated optimizations and simple linting to sophisticated analyses for bug detection, security, and program understanding. In contrast, static analysis for hardware remains underexploited, resembling the early state of software analysis. Most existing hardware static analyses are confined to compiler optimizations and linting, lacking the sophistication needed to uncover complex design flaws. Furthermore, we observe that many hardware bugs reported in recent literature could have been identified by sophisticated static analyses that account for hardware-specific semantics and data flow; however, such bug detection analyses are absent today. To exploit the untapped potential of sophisticated hardware analysis, we present a series of bug detection analyses for Verilog, the predominant hardware description language (HDL). Moreover, these analyses are built upon our fundamental analyses that capture essential hardware-specific characteristics—such as bit-vector arithmetic, register synchronization, and digital component concurrency—and enable the examination of hardware data and control flows. Together, these analyses form a well-organized analysis suite with a modular design, in which diverse fundamental analyses combine to support bug detection, hardware understanding, and other potential clients. To implement these analyses, we further offer dedicated infrastructure, including a Verilog front end, an intermediate representation (IR) for analysis, and an analysis manager. To validate the utility of our analyses, we applied them to real-world hardware projects. Unlike software, real-world hardware projects tend to contain fewer but harder-to-detect bugs, as they typically undergo extensive simulation and rigorous verification to prevent the prohibitive costs of hardware defects. Despite this, our preliminary experimental results are highly promising: applying these proposed analyses to popular real-world Verilog projects (averaging 1.5K+ GitHub stars) uncovered nine previously unknown bugs, all confirmed by developers; moreover, we successfully identified a total of 18 bugs beyond the capabilities of existing static analyses for Verilog bug detection (i.e., linters). These results underscore the transformative potential of sophisticated static analysis in hardware design. Our analysis suite and infrastructure are also highly reusable: on average, each bug-detection client built on our analysis suite requires about 270 LoC, compared to 5,700 LoC when developed from scratch. By open-sourcing the entire system, involving substantial engineering effort (100K+ LoC), we aim to encourage further innovation and applications of sophisticated static analysis for hardware, hopefully fostering a similarly vibrant ecosystem that software analysis enjoys.

This program is tentative and subject to change.

Wed 17 Jun

Displayed time zone: Mountain Time (US & Canada) change

14:00 - 15:40
Static Analysis 1PLDI Research Papers at Flatirons 4
14:00
20m
Talk
Flow-Analysis-Based Closure Optimization
PLDI Research Papers
John Reppy University of Chicago, Olin Shivers Northeastern University, Byron Zhong University of Chicago
DOI Pre-print
14:20
20m
Talk
SSA without Dominance for Higher-Order Programs
PLDI Research Papers
Roland Leißa University of Göttingen, Johannes Griebler University of Göttingen
DOI Pre-print
14:40
20m
Talk
Analyzing Bytes: Pre-Disassembly Static Binary Analysis
PLDI Research Papers
Huan Nguyen Google, Soumyakant Priyadarshan Stony Brook University, Chencheng Jiang Stony Brook University, R. Sekar Stony Brook University
DOI
15:00
20m
Talk
Exploiting Sophisticated Static Analysis for Verilog
PLDI Research Papers
Qinlin Chen Nanjing University, Nairen Zhang Nanjing University, Jinpeng Wang Nanjing University, Jiacai Cui Nanjing University, Tian Tan Nanjing University, Xiaoxing Ma Nanjing University, Chang Xu Nanjing University, Jian Lu Nanjing University, Yue Li Nanjing University
DOI