Towards Hardware Implementation of Neural Network-based Communication Algorithms
This addresses the gap between simulation-based research and practical hardware deployment for communication systems, though it is incremental as it focuses on quantization and fixed-point adaptation of existing methods.
The paper tackles the problem of implementing neural network-based communication algorithms on specialized hardware like FPGAs and ASICs, demonstrating that fixed-point arithmetic with quantized weights achieves negligible performance loss while enabling higher inference rates and reduced complexity.
There is a recent interest in neural network (NN)-based communication algorithms which have shown to achieve (beyond) state-of-the-art performance for a variety of problems or lead to reduced implementation complexity. However, most work on this topic is simulation based and implementation on specialized hardware for fast inference, such as field-programmable gate arrays (FPGAs), is widely ignored. In particular for practical uses, NN weights should be quantized and inference carried out by a fixed-point instead of floating-point system, widely used in consumer class computers and graphics processing units (GPUs). Moving to such representations enables higher inference rates and complexity reductions, at the cost of precision loss. We demonstrate that it is possible to implement NN-based algorithms in fixed-point arithmetic with quantized weights at negligible performance loss and with hardware complexity compatible with practical systems, such as FPGAs and application-specific integrated circuits (ASICs).