The insights from this work can guide the design of future CNN hardware accelerators with more flexible and efficient memory subsystems in the following ways:
Customized Memory Hierarchies: Designing hardware accelerators with customizable memory hierarchies that can adapt to the specific memory access patterns of different layers in a CNN. This customization can optimize memory utilization and reduce energy consumption.
Dynamic Memory Allocation: Implementing dynamic memory allocation schemes that allocate memory resources based on the requirements of each layer during inference. This dynamic allocation can improve memory efficiency and overall performance.
Adaptive Quantization Strategies: Incorporating adaptive quantization strategies that adjust the precision of weights and activations based on the layer characteristics and hardware constraints. This adaptability can enhance energy efficiency and accuracy trade-offs.
Efficient Data Movement: Optimizing data movement within the memory subsystem to minimize data transfers and maximize memory bandwidth utilization. Efficient data movement can reduce latency and energy consumption in CNN accelerators.
By integrating these insights into the design of future CNN hardware accelerators, it is possible to create more versatile, energy-efficient, and high-performance systems that can meet the demands of complex CNN models effectively.