Reconfigurable Computing
Reconfigurable computing represents a powerful paradigm that bridges the gap between the flexibility of software and the performance of dedicated hardware. By utilizing devices whose structure and function can be modified after fabrication, reconfigurable systems adapt their computational architecture to match the requirements of different applications, achieving performance levels approaching custom silicon while retaining the programmability of general-purpose processors.
This field encompasses the architectures, design methodologies, and programming models that enable adaptive hardware systems. From field-programmable gate arrays serving as standalone accelerators to tightly integrated heterogeneous platforms combining multiple processor types, reconfigurable computing provides solutions for applications demanding both high performance and implementation flexibility.
Topics in Reconfigurable Computing
Adaptive Computing
Respond to changing requirements. This section covers self-modifying hardware, evolutionary hardware, fault-tolerant reconfiguration, performance monitoring, dynamic optimization, and machine learning acceleration.
Coarse-Grained Arrays
Use word-level reconfiguration. Topics include CGRA architectures, routing networks, configuration memories, mapping algorithms, scheduling, pipelining, and power management.
Dynamic Reconfiguration
Change functionality at runtime. This section addresses partial reconfiguration, context switching, configuration management, bitstream compression, configuration caching, self-reconfiguration, and virtual hardware.
Heterogeneous Computing
Combine different processing elements. Coverage encompasses CPU-FPGA systems, GPU-FPGA integration, hardware accelerators, coherent interconnects, shared memory systems, task partitioning, and workload distribution.
About Reconfigurable Computing
The concept of reconfigurable computing emerged from the recognition that fixed-function hardware, while fast, lacks the adaptability needed for evolving applications, while software running on general-purpose processors sacrifices performance for flexibility. Reconfigurable devices, particularly FPGAs, occupy a middle ground where the computational fabric can be customized for specific algorithms while remaining changeable throughout the system's operational lifetime.
Modern reconfigurable computing extends beyond simple FPGA-based acceleration to encompass sophisticated heterogeneous systems integrating multiple processor types, advanced programming models that abstract hardware complexity, and runtime systems that dynamically adapt hardware configurations to changing workloads. These advances have made reconfigurable computing essential in domains ranging from high-performance computing and artificial intelligence to telecommunications and embedded systems.