3. Conclusion

In this chapter, various computation hardware platforms for machine learning algorithms are discussed. Among them, GPU is the most widely used one due to its fast computation speed and compatibility with various algorithms. FPGA shows better energy efficiency compared with GPU when computing machine learning algorithm at the cost of low speed. Finally, different ASIC architectures are proposed to support certain kinds of the machine learning algorithms such as a deep convolutional neural network with model compression technique to improve hardware performance. Compared with the GPU and FPGA, ASIC shows the best energy efficiency and computation speed, however, at the cost of reconfigurability to various ML algorithms. Depending on the specific applications, the designers should select the most suitable computation hardware platform.
