Efficient Hardware Architectures for Accelerating Deep Neural Networks

The rapid advancement of Deep Neural Networks (DNNs) has posed significant challenges in computational ef- ficiency, memory access, and energy consumption. This paper explores efficient hardware architectures tailored for DNN accel- eration, focusing on their integration within modern computer architecture. Specialised hardware accelerators such as FPGAs and ASICs are analysed for their role in optimising parallelism, dataflow, and memory hierarchy. Architectural techniques such as systolic arrays, tensor processing units, and processing-in- memory (PIM) are evaluated for their impact on performance and power efficiency. Furthermore, memory bandwidth con- straints and cache optimisations are discussed in relation to their influence on instruction-level and thread-level parallelism. Experimental comparisons highlight how architectural enhance- ments, including sparsity-aware computations and approximate computing, contribute to high-throughput, low-power AI pro- cessing. Future research directions include the convergence of neuromorphic computing and emerging memory technologies to further enhance efficiency in AI-driven architectures. This paper provides valuable insights into the intersection of deep learning acceleration and modern computer architecture design.