Software and Hardware Co-optimization for Deep Learning Algorithms on FPGA
Skip to main content
Open Access Publications from the University of California


UCLA Electronic Theses and Dissertations bannerUCLA

Software and Hardware Co-optimization for Deep Learning Algorithms on FPGA


Over recent years, deep learning paradigms such as convolutional neural networks (CNNs) have shown great success in various families of tasks including object detection and au- tonomous driving, etc. To extend such success to non-euclidean data, graph convolutional networks (GCNs) have been introduced, and have quickly attracted industrial and academia attention as a popular solution to real-world problems. However, both CNNs and GCNs often have huge computation and memory complexity, which calls for specific hardware architec- tures to accelerate these algorithms. In this dissertation, we propose several architectures to accelerate CNNs and GCNs based on FPGA platforms. We start from the domain-specific FPGA-overlay processor (OPU) on commonly used CNNs, such as VGG, Inception, ResNet, and YoloV2. The data is first quantized to 8-bit fixed-point with little accuracy loss to reduce computation complexity and memory require- ment. A fully-pipelined dataflow architecture is proposed to accelerate the typical layers (i.e., convolutional, pooling, residual, inception, and activation layers) in CNNs. Experi- mental results show that OPU is 9.6� faster than GPU Jetson TX2 on a cascaded of three CNNs, which are used for the curbside parking system. However, 8-bit fixed-point data representation always need re-training to maintain accu- racy for deep CNNs. In this way, we propose a low precision (8-bit) floating-point (LPFP) quantization method for FPGA-based acceleration to overcome the above limitation. With- out any re-training, LPFP finds an optimal 8-bit data representation with negligible top- 1/top-5 accuracy loss (within 0.5%/0.3% in our experiments, respectively, and significantly better than existing methods for deep CNNs). Furthermore, we implement one 8-bit LPFP multiplication by one 4-bit multiply-adder (MAC) and one 3-bit adder. Therefore, we can implement four 8-bit LPFP multiplications using one DSP48E1 of Xilinx Kintex-7 family or one DSP48E2 of Xilinx Ultrascale/Ultrascale Plus family whereas one DSP can only imple- ment two 8-bit fixed-point multiplications. Experiments on six typical CNNs for inference show that on average, we improve throughput by 1.5� over existing FPGA accelerators. Particularly for VGG16 and Yolo, compared with seven FPGA accelerators, we improve average throughput by 3.5� and 27.5� and average throughput per DSP by 4.1� and 5�, respectively. CNNs quantized with mixed precision, on the other hand, benefits from low precision while maintaining accuracy. To better leverage the advantages of mixed precision, we propose a Mixed Precision FPGA-based Overlay Processor (MP-OPU) for both conventional and lightweight CNNs. The micro-architecture of MP-OPU considers sharing of computation core with mixed precision weights and activations to improve computation efficiency. In addition, run-time scheduling of external memory access and data arrangement are optimized to further leverage the advantages of mixed precision data representation. Our experimental results show that MP-OPU reaches 4.92 TOPS peak throughput when implemented on Xilinx VC709 FPGA (with all DSPs configured to support 2-bit multipliers). Moreover, MP-OPU achieves 12.9� latency reduction and 2.2� better throughput per DSP for conventional CNNs, while 7.6� latency reduction and 2.9� better throughput per DSP for lightweight CNNs, all on average compared with existing FPGA accelerators/processors, respectively. Graph convolutional networks (GCNs) have been introduced to effectively process non-euclidean graph data. However, GCNs incur large amount of irregularity in computation and memory access, which prevents efficient use of previous CNN accelerators/processors. In this way, we propose a lightweight FPGA-based accelerator, named LW-GCN, to tackle irregularity in computation and memory access in GCN inference. We first decompose the main GCN operations into Sparse Matrix-Matrix Multiplication (SpMM) and Matrix-Matrix Multiplication (MM). Thereafter, we propose a novel compression format to balance work- load across PEs and prevent data hazards. In addition, we quantize the data into 16-bit fixed-point and apply workload tiling, and map both SpMM and MM onto a uniform archi- tecture on resource limited devices. Evaluations on GCN and GraphSAGE are performed on Xilinx Kintex-7 FPGA with three popular datasets. Compared with existing CPU, GPU and state-of-the-art FPGA-based accelerator, LW-GCN reduces latency by up to 60�, 12� and 1.7� and increases power efficiency by up to 912�, 511� and 3.87�, respectively. Moreover, compared with Nvidia’s latest edge GPU Jetson Xavier NX, LW-GCN achieves speedup and energy savings of 32� and 84�, respectively. At last, we extend our GCN inference accelerator to a GCN training accelerator, called SkeletonGCN. To better fit the properties of GCN training, we add more software-hardware co-optimizations. First, we simplify the non-linear operations in GCN training to better fit the FPGA computation, and identify reusable intermediate results to eliminate redundant computation. Second, we optimize the previous compression format to further reduce mem- ory bandwidth while allowing efficient decompression on hardware. Finally, we propose a unified architecture to support SpMM, MM and MM with transpose, all on the same group of PEs to increase DSP utilization on FPGA. Evaluations are performed on Xilinx Alveo U200 board. Compared with existing FPGA-based accelerator on the same network archi- tecture, SkeletonGCN can achieve up to 11.3� speedup while maintaining the same training accuracy with 16-bit fixed-point data representation. In addition, SkeletonGCN is 178� and 13.1� faster than state-of-the-art CPU and GPU implementation on popular datasets, respectively. To summarize, we have been working on FPGA-based acceleration for deep learning algorithms of CNNs and GCNs in both inference and training process. All the accelera- tors/processors were hand-coded and have been fully verified. In addition, the related tool chains for generating golden results and running instructions for the accelerators/processors have also been finished.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View