- The paper categorizes diverse FPGA mapping frameworks for CNNs, emphasizing unique architectural designs and trade-offs in performance and power efficiency.
- It compares streaming architectures with single computation engines, highlighting differences in parallelism, latency, and compilation overhead.
- The study outlines future directions by advocating for integration with modern deep learning infrastructures and support for FPGA-based training to enhance power-sensitive AI applications.
Overview of Toolflows for Mapping CNNs onto FPGAs: A Survey and Future Directions
The paper by Venieris et al. offers an exhaustive survey on the methods and frameworks available for deploying Convolutional Neural Networks (CNNs) onto Field-Programmable Gate Arrays (FPGAs). Over the past decade, CNNs have emerged as prominent tools excelling in multiple AI tasks, ranging from image classification to navigation in autonomous vehicles. Yet, the deployment of CNNs often gravitates towards GPUs and CPUs which, despite their computational prowess, are notably power-hungry. FPGAs present themselves as a viable alternative, offering a unique balance between performance, power efficiency, and programmability.
Herein, the paper categorizes the existing CNN-to-FPGA mapping frameworks into architectures with distinct design features. These include streaming architectures, where each layer receives a dedicated compute block, and single computation engines, characterized by a shared processing unit handling different layers sequentially. Streaming architectures, as seen in tools like fpgaConvNet and DeepBurning, promise high parallel performance but at the cost of longer compilation times. In contrast, single computation engine designs prioritize flexibility, with platforms like Angel-Eye and FP-DNN simplifying bitstream reuse between models.
Framework Characteristics and Analysis
The frameworks discussed exhibit variability across several dimensions:
- Supported Neural Network Models: Most frameworks primarily target conventional CNNs; however, some like DeepBurning and FP-DNN extend support to Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, reflecting the evolving demands of deep learning models.
- Interface Options: Integration with popular deep learning platforms such as Caffe and TensorFlow equips these frameworks to align more closely with established AI workflows. However, user adoption can be limited by the complexity and customization of different FP interfaces.
- Design Space Exploration: The effectiveness of design space exploration (DSE) methodologies plays a crucial role in toolflows' eventual performance outputs. Approaches that encompass thorough parameter space exploration often yield designs optimized for specific performance metrics. For instance, fpgaConvNet and DnnWeaver adopt heuristic and global optimization techniques, achieving fine-grained exploration.
- Arithmetic Precision: Precision tuning stands as a method to balance power and performance. Some frameworks, like Angel-Eye, employ dynamic quantisation, whereas others like Finn use binarization, targeted towards highly efficient Binarized Neural Networks (BNNs).
Performance Metrics and Tool Comparisons
The comparative discussion on toolflow performance underscores marked variations in throughput and latency achieved across different hardware platforms. Notably, while high-throughput designs like Caffeine and fpgaConvNet capitalize on single computation engines to exploit computational redundancy in CNNs, frameworks such as Angel-Eye achieve reduced latency that is critical for real-time applications.
Future Directions
The progression of CNN models towards deeper, more complex structures continues to challenge the adaptability of FPGA toolflows. With newer AI techniques introducing diverse challenges such as irregular layer connectivity and increased intricacy (e.g., GoogLeNet, ResNet), future solutions must pivot towards accommodating these complexities. Moreover, efficient integration with emerging deep learning infrastructures like PyTorch and MXNet can democratize FPGA usage further among AI practitioners.
Furthermore, advancing the support for training on FPGAs can reap significant benefits given their power efficiency compared to traditional platforms. Leveraging FPGAs for training can democratize distributed neural network model development, especially for power-sensitive and embedded edge devices.
Conclusion
The paper elucidates the strengths of FPGA-based toolflows while proposing salient features and techniques to improve performance and accessibility. As computational demands intensify, these insights form the foundational groundwork for evolving reconfigurable computing paradigms, aligning closer with real-world needs and driving innovation across AI and embedded systems domains.