The field of deep learning accelerators is rapidly evolving, with a focus on improving energy efficiency while maintaining performance. Researchers are exploring innovative approaches, such as sparse neural networks, lookup table-based multiplication, and approximate computing, to reduce power consumption without sacrificing accuracy. These techniques are being applied to various applications, including wireless communication systems, image processing, and natural language processing. Notably, the use of field-programmable gate arrays (FPGAs) is becoming increasingly popular due to their ability to provide high performance and low power consumption.

Some noteworthy papers in this area include: SparseDPD, which introduces an FPGA accelerator employing a spatially sparse phase-normalized time-delay neural network for digital predistortion, achieving exceptional linearization performance with low power consumption. MEDEA, a design-time multi-objective manager for energy-efficient DNN inference on heterogeneous ultra-low power platforms, which achieves overall energy reductions of up to 38% compared to state-of-the-art methods. FINN-GL, which enables the generalized deployment of LSTMs on FPGAs, achieving a balance between performance and resource consumption while matching state-of-the-art models with reduced precision. MAx-DNN, which explores the interplay of fine-grained error resilience of DNN workloads with hardware approximation techniques, delivering up to 54% energy gains in exchange for up to 4% accuracy loss.