The recent advancements in the field of deep neural network (DNN) optimization and acceleration have been notably focused on improving efficiency, energy consumption, and accuracy, particularly for resource-constrained environments. Innovations in quantization techniques, systolic array architectures, and accelerator designs have led to significant improvements in both hardware performance and model accuracy. Sub-6-bit quantization methods, such as those leveraging simple shifting-based operations and Huffman coding, have demonstrated substantial accuracy gains over traditional methods. Systolic array architectures, with novel dataflow designs eliminating synchronization requirements, have shown enhanced throughput and energy efficiency, particularly in transformer workloads. Additionally, novel DNN accelerators integrating asymmetric quantization and bit-slice sparsity have achieved high accuracy and hardware efficiency, outperforming existing solutions. These developments collectively indicate a shift towards more efficient, scalable, and energy-conscious designs in DNN hardware acceleration, catering to the growing demands of AI workloads across various domains.