Programming Languages and Software Engineering¶
Language design and semantics¶
Solution: user-friendly, resource-efficient, and secure programming languages
Compiler construction and optimization¶
Solution: improving performance, reducing resource usage, and ensuring correctness
Deep Learning Compilers¶
Solution: Graph transformations, Kernel fusion, Tensor optimization for compute and memory
Year | Venue | Authors | Title | Tags | P | E | N |
---|---|---|---|---|---|---|---|
2018 | OSDI | UW | TVM: An Automated End-to-End Optimizing Compiler for Deep Learning | operator fusion; graph-level DL compiler; automatic code generation; tensor expression simplification | 4 | 4 | 4 |
2025 | PPoPP | Thu | FlashTensor: Optimizing Tensor Programs by Leveraging Fine-grained Tensor Property | dataflow centered code recognition and optimization; two-stage heuristic algorithm to optimize tensor computation; kernel fusion | 4 | 4 | 2 |
Domain-Specific Languages¶
Solution: formal semantics definition, tool generation automation, cross-domain generalization
Sparse Tensor Algebra Compilers¶
Solution: multi-format iteration efficiency, format combination optimization, architecture-agnostic code generation
Year | Venue | Authors | Title | Tags | P | E | N |
---|---|---|---|---|---|---|---|
2018 | OOPSLA | MIT | Format Abstraction for Sparse Tensor Algebra Compilers | coordinate hierarchies; level formats abstraction; property-based merge lattice optimizations; level iterator conversion | 4 | 4 | 3 |
2020 | PLDI | MIT | Automatic Generation of Efficient Sparse Tensor Format Conversion Routines | coordinate remapping notation; attribute query language; tensor assembly abstract interface; three-phase conversion decomposition | 4 | 4 | 4 |
2022 | OOPSLA | MIT | Compilation of Dynamic Sparse Tensor Algebra | node schema language; assembly abstract interface; map function generation; iterator optimization; dynamic tensor format composition | 4 | 4 | 3 |
Hardware Description Languages¶
Solution: expressive hardware specification, efficient simulation and synthesis, robust verification methodologies
Streaming Computation Models¶
Solution: high-throughput data processing, real-time analytics, efficient resource utilization for continuous data
Year | Venue | Authors | Title | Tags | P | E | N |
---|---|---|---|---|---|---|---|
2020 | ASPLOS | Stanford | Fleet: A Framework for Massively Parallel Streaming on FPGAs | user write serial code for parallel; multi-stream parallelism; ready-valid signaling | 3 | 4 | 3 |
HLS Code Generation and Automation¶
Solution: bridging high-level languages to hardware, design space exploration, QoR improvement automation
General HLS Compiler¶
C/C++/SystemC to RTL, microarchitecture optimization, resource sharing and scheduling
Year | Venue | Authors | Title | Tags | P | E | N |
---|---|---|---|---|---|---|---|
2022 | ASPLOS | UCLA | HeteroGen: transpiling C to heterogeneous HLS code with automated test generation and program repair | automated test generation; dependence-guided search space pruning; early candidate rejection using coding styles | 3 | 4 | 3 |
2022 | HPCA | UIUC | ScaleHLS: A New Scalable High-Level Synthesis Framework on Multi-Level Intermediate Representation | multi-level IR for HLS; HLS-dedicated analysis/transform library; MLIR-based HLS framework | 4 | 4 | 4 |
2022 | FPGA | Cornell | HeteroFlow: An Accelerator Programming Model with Decoupled Data Placement for Software-Defined FPGAs | Decoupled data placement; Unified data placement primitive; Multi-level memory hierarchy optimization | 4 | 4 | 4 |
2024 | DATE | UIUC | Subgraph Extraction-Based Feedback-Guided Iterative Scheduling for HLS | ISDC iterative SDC scheduling; subgraph extraction-based low-level feedback; fanout and window-based subgraph extraction mechanism | 4 | 4 | 4 |
2025 | FPGA | University of Glasgow | Dynamic Loop Fusion in High-Level Synthesis | Dynamic loop fusion; HLS; Irregular memory access; Address monotonicity; Decoupled Access/Execute (DAE); Program-order schedule; Data Unit (DU) | 4 | 4 | 4 |
HLS Verification and Testing¶
Solution: automated bug detection, verification dataset generation, LLM-aided debugging for HLS designs
Year | Venue | Authors | Title | Tags | P | E | N |
---|---|---|---|---|---|---|---|
2024 | LAD | UIUC | An Iteratively-refined Dataset for High-Level Synthesis Functional Verification through LLM-Aided Bug Injection | Chrysalis dataset with bug injection; ICL+RAG+CoT bug injection methodology; iteratively-refined HLS verification dataset | 4 | 4 | 4 |
Dataflow HLS acceleration¶
Solution: exploiting task-level parallelism, optimizing inter-kernel communication, maximizing pipeline throughput
Year | Venue | Authors | Title | Tags | P | E | N |
---|---|---|---|---|---|---|---|
2024 | ASPLOS | UIUC | HIDA: A Hierarchical Dataflow Compiler for High-Level Synthesis | hierarchical dataflow IR (HIDA-IR); multi-level dataflow optimizer (HIDA-OPT); pattern-driven task fusion | 3 | 5 | 4 |
2025 | FPGA | UCLA | Stream-HLS: Towards Automatic Dataflow Acceleration | automatic dataflow HLS; global scheduling for streaming; MINLP for HLS optimization | 4 | 4 | 4 |
HLS for specialized hardware¶
Solution: target-specific code generation, custom memory interface synthesis, co-optimization with physical constraints
Year | Venue | Authors | Title | Tags | P | E | N |
---|---|---|---|---|---|---|---|
2023 | FPGA | UoP | DONGLE: Direct FPGA-Orchestrated NVMe Storage for HLS | HLS direct NVMe access; FPGA-orchestrated storage; Unified HLS storage interface; Single-source HLS for storage; DONGLE architecture | 4 | 4 | 4 |
2023 | FPGA | HKUST | FADO: Floorplan-Aware Directive Optimization for High-Level Synthesis Designs on Multi-Die FPGAs | Floorplan-aware HLS; Multi-die FPGA optimization; Directive-floorplan co-optimization; Incremental floorplanning for HLS; MMBP for HLS DSE | 3 | 4 | 4 |
2025 | FPGA | Brown University | ARIES: An Agile MLIR-Based Compilation Flow for Reconfigurable Devices with AI Engines | MLIR-based AIE compilation; Unified AIE+PL IR; Tile-based parallelism; ADF dialect; Automated AIE placement | 4 | 5 | 4 |
Program analysis¶
Solution: statically or dynamically analyzing programs to understand their behavior, detect errors, and optimize performance
Domain-specific program analysis¶
Solution: leveraging domain knowledge for precise analysis, specialized bug detection, targeted optimization insights
HLS program analysis¶
Solution: verifying functional correctness of HLS, analyzing performance bottlenecks, ensuring interface compatibility
Year | Venue | Authors | Title | Tags | P | E | N |
---|---|---|---|---|---|---|---|
2025 | FPGA | UoE | Latency Insensitivity Testing for Dataflow HLS Designs | Automated Latency Insensitivity Testing; Parallel Hardware-Accelerated Testing Platform; Test space reduction; Stalling Units (SU) | 4 | 4 | 4 |