FPGA-GPU Co-Design for Scientific Workloads
Delivered a novel compute paradigm that bridged programmable hardware and GPU acceleration, enabling domain-specific optimization beyond traditional supercomputing architectures.
Situation
Scientific workloads involving protein synthesis and cryptographic modeling required deterministic performance and highly optimized execution paths. General-purpose compute architectures introduced inefficiencies due to abstraction layers and lack of workload-specific optimization.
Solution
Architected a co-design framework integrating programmable logic with GPU acceleration. Critical algorithms were implemented directly in hardware logic while leveraging GPUs for complementary parallel tasks.
OUTCOMES
Challenges
Efficiency
- •Generalized compute cost
- •Inefficient abstraction layers
Determinism
- •Inconsistent execution timing
- •Limited repeatability guarantees
Optimization
- •Lack workload specialization
- •Constrained algorithm mapping
Solutions
Deterministic FPGA Execution
FPGA-based execution for deterministic, low-latency computation.
- Implemented hardware-native execution for critical algorithms
- Reduced latency through direct logic-level processing
Parallel GPU Acceleration
GPU-based acceleration for massively parallel workloads.
- Offloaded large-scale parallel workloads to GPUs
- Complemented FPGA pipelines with flexible execution capacity
Custom Data Pipelines
Custom data pipelines between heterogeneous components.
- Designed high-speed interfaces between FPGA and GPU systems
- Reduced data transfer bottlenecks across compute layers
- Enabled coordinated heterogeneous workload execution
- Improved overall pipeline efficiency
Hardware Logic Validation
Simulation environments capable of validating hardware-level logic prior to deployment.
- Built simulation environments for pre-deployment validation
- Verified hardware logic before production integration
- Reduced deployment risk for specialized execution paths
- Accelerated iteration across hardware designs