Support Vector Machines (SVMs) have proven to yield high accuracy and have been used widespread in recent years. However, the standard versions of the SVM algorithm are very time-consuming and computationally intensive; which places a challenge on engineers to explore other hardware architectures than CPU, capable of performing real-time training and classifications while maintaining low power consumption in embedded systems. This paper proposes an overview of works based on the two most popular parallel processing devices: GPU and FPGA, with a focus on multiclass training process. Since different techniques have been evaluated using different experimentation platforms and methodologies, we only focus on the improvements realized in each study.

1. HARDWARE ACCELERATION OF SVM TRAINING
FOR REAL-TIME EMBEDDED SYSTEMS: AN
OVERVIEW
Ilham Amezzane
Ibn Tofail University
March 26th, 20181

2. Outline
 Background
 Accelerating SVM Training with:
 GPU
 FPGA
 GPU vs FPGA Performance Comparison
 Conclusion
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3.  Smartphone-based Applications :
 Healthcare
 Smart Homes
 WSNs
 Challenges:
 Large datasets
 Needs of accelerating the processing speed
 Limited resources
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Real-time Embedded Applications

4. Support Vector Machines (SVM)
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 Instance-based:
 Optimal hyperplane for linearly separable patterns.
 Strength:
• Can apply linear classification techniques to non-linear data using the kernel trick.
• High accuracy
 Weakness:
• Memory-intensive
• Hard to interpret

5.  Quadratic Programming (QP):
 size grows with the number of training samples : of O(N2) complexity.
 Several decomposition methods:
 e.g. Sequential Minimal Optimization (SMO)
 CPU standard version (LIBSVM):
 SMO based
 For real-time applications, can be :
 very time-consuming
 computationally intensive
SVM Training Algorithm: Limitations
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6. Outline
 Background
 Accelerating SVM Training with:
 GPU
 FPGA
 GPU vs FPGA Performance Comparison
 Conclusion
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7. Graphic Processing Unit (GPU)
 Computer intensive
 Highly-parallel computation
 More data processing than caching and flow control
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https://www.carestream.com/blog/wp-content/uploads/2015/09/CSH_CPU-GPU_Illustration.png

8. GPU Programming Frameworks
 CUDA:
 NVIDIA
 OpenCL:
 AMD (CPUs, GPUs),
 Intel (CPUs, GPUs),
 Nvidia (GPUs),
 Qualcomm (embedded/mobile CPUs)
 ALTERA (FPGAs),
OpenCL allows heterogeneous computation in one system.
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9. (2008, 2010)/ Works based on modified SMO algorithm of the standard LibSVM:
 Dataset dependent speedups
(2011)/ Works based on pre-calculating the kernel matrix elements:
 Combining the CPU and the GPU
 GPU speed has higher impact on the total training time.
(2011)/ New package GPUSVM :
 a CV tool, a fast training tool and a predicting tool.
 2.27 – 77 times faster
(2013)/ A novel implementation to accelerate the CV procedure :
 Running multiple training tasks simultaneously
 10- 100 times faster.
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Research Works with GPU

10. 10
(2015)/ Heterogeneous computing system
 OpenCL framework
 9- 22 times faster.
(2016)/ Converting a gradient-ascent based algorithm to a GPU implementation:
 Fastest for high-dimensional feature vectors.
(2016)/ Accelerating the CV process:
 OpenCL framework
 Applied in a mobile device
 1.5 times faster
Research Works with GPU

11.  Dense matrix format
 For storing datasets
 RBF kernel
 Without the possibility of changing the used kernel easily
 Binary classification
 In most cases
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Limitations

12. Outline
 Background
 Accelerating SVM Training with:
 GPU
 FPGA
 GPU vs FPGA Performance Comparison
 Conclusion
12

13.  Parallelism & Pipelining
 High performance
 Reconfigurability
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Field-programmable Gate Array (FPGA)
Generic FPGA Architecture

14. FPGA
 Typical approaches to speed up the SVM computations :
 Increasing the level of parallelism
 exploiting the inherent parallelism of the SVM algorithm.
 Reducing the bit width of the data representation
 reducing the resource usage.
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15. (2008)/ A scalable FPGA architecture based on Gilbert’s algorithm:
 Partitioned into floating-point and fixed-point domains.
 3 orders of magnitude faster than SW implementation.
(2011)/ A novel architecture for the SMO process:
 With a memory block and a cache block
 A decrease in processing time from using the cache
(2011)/ Modular design improved:
 90% reduction in training time
(2014)/ A novel reconfigurable chip design for accelerating SMO :
 Reconfigurable architectures.
 Dynamic scheduling for an efficient reconfiguration.
 Power consumption (17 times )
 Training speed (16 times )
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Research works with FPGA

16. Research works with FPGA
(2015)/ First floating-point based and multi-use reconfigurable HW: R2SVM
 Modifications of the number of classes/features.
 Modifications of kernel selection and parameters at run-time.
 Extensive pipelining and parallelism.
 Examined in a human-computer wireless interface
 Operating at a very low power level.
(2016)/ A novel optimised dataflow architecture for incremental SVM training:
 Up to 40.97 times faster.
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17. Outline
 Background
 Accelerating SVM Training with:
 GPU
 FPGA
 GPU vs FPGA Performance Comparison
 Conclusion
17

18. Feature Analysis Winner
Floating-point
Processing
Total Flops of GPUs > the best FPGAs’ GPU
Timing Latency Deterministic timing in FPGAs, with latencies < GPUs FPGA
Processing/Watt FPGAs are 3-4 times better in terms of GFLOPS per watt FPGA
Backward
Compatibility
FPGA HDL can be moved to newer platforms, but with some
reworking.
GPU
Flexibility FPGA lacks flexibility to modify the hardware implementation of
the synthesized code.
GPU
Size FPGA’s lower power consumption (smaller dimensions). FPGA
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GPU vs FPGA Performance Comparison
http://www.bertendsp.com/pdf/whitepaper/BWP001_GPU_vs_FPGA_Performance_Comparison_v1.0.pdf

19. Outline
 Background
 Accelerating SVM Training with:
 GPU
 FPGA
 GPU vs FPGA Performance Comparison
 Conclusion
19

20.  GPUs and FPGAs can offer significant improvements to the SVM
training time without scarifying recognition accuracy.
 Power management techniques are extremely important to ensure
longevity and reliability of GPUs in embedded systems.
 A single platform cannot be considered as most energy efficient for all
possible applications.
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Conclusion

21. References
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