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CmabSyn

Logic Synthesis Compilation Autotuning through Combinatorial Multi-Armed Bandits

Env dependencies:

  1. abc 1.01
  2. OpenMP 4.5
  3. gcc 11.4.0
  4. Yosys 0.39
mkdir build
cd build
cmake ..
make

Directory Structure

CmabSyn/
├── README.md                     # Documentation and an overview of the project.
├── test.png                      # Example showing the optimization process.
├── benchmarks/                   # Includes three benchmark datasets used for testing and optimization.
│   ├── arithmetic/               # Arithmetic circuits 
│   ├── VTR_benchmarks/           # VTR (Verilog-to-Routing) circuits 
│   └── SCE_benchmarks/           # SCE (superconducting electronic) circuits 
├── optimized_benchmarks/         # Contains optimized versions of the benchmark datasets for various objectives.
│   ├── 1.VTR_benchmarks_Logic_optimized/    # Logic-level optimizations performed on the VTR benchmarks.
│   ├── 2.SCE_benchmarks_Logic_optimied/     # Logic-level optimizations performed on the SCE benchmarks.
│   ├── 3.VTR_benchmarks_nangate45nm/        # VTR benchmarks optimized for Nangate 45nm technology mapping.
│   ├── 4.VTR_benchmarks_asap7nm/            # VTR benchmarks optimized for ASAP 7nm technology mapping.
│   ├── 5.VTR_benchmarks_FPGA/               # VTR benchmarks optimized specifically for FPGA mapping.
│   └── 6.arithmetic_benchmarks_FPGA/        # Arithmetic benchmarks optimized specifically for FPGA mapping.
└── src/                          # Source code for the project, including all logic synthesis and optimization implementations.

Our Work

CmabSyn: An Optimization Framework for Logic Synthesis

CmabSyn is a logic synthesis optimization framework based on the Combinatorial Multi-Armed Bandit (CMAB) algorithm. It tackles the challenges of exploring vast search spaces and avoiding local optima in circuit optimization by:

  • Dependency-Aware Optimization: Introducing dependency relations through arm combinations and coordinating optimization operators with immediate and long-term reward strategies.
  • Learning with Linear Rewards: Leveraging domain-specific knowledge to balance exploration and exploitation for decision-making.
  • Parallelization: Significantly enhancing efficiency and reducing runtime.

Key results include up to 14.47% area improvement and 1.38× runtime acceleration for the ASAP 7nm standard cell library. CmabSyn demonstrates superior performance in logic optimization, FPGA LUT mapping, and ASIC technology mapping.

Optimization Example

(Logic Optimization for C5315.blif)

example