Energy-Efficient CPU+FPGA-Based CNN Architecture for Intrusion Detection Systems

Machine learning applications have their viability strongly linked to the ability of computer architectures to offer high performance and energy efficiency. Although different architectures can offer high computational performance, they may lack energy efficiency, which is crucial for consumer-based applications. For instance, intrusion detection systems can use machine learning techniques to monitor network traffic and identify possible malicious activities. These systems are constantly active on devices such as firewalls, reinforcing the need for energy efficiency, e.g., in smart homes and autonomous vehicles. Field-programmable gate array (FPGA) can offer better energy efficiency than other architectures. Considering that, we designed and evaluated a CPU+FPGA-based convolutional neural network for intrusion detection systems. We deployed our strategy in a heterogeneous CPU (Intel Xeon) + FPGA (Arria 10) platform. Then, we compared the proposed architecture with its respective parallel software version for power, energy, and performance evaluation. The NSL-KDD dataset was used for intrusion detection benchmarking. The energy efficiency results for CNN showed up to $\mathbf {4.5\times }$ more operations per watt than its software version.