Understanding Memory Hierarchies In Embedded AI: From Cache To DDR

Abstract

The evolution of artificial intelligence applications within embedded systems has fundamentally altered memory architecture design paradigms, necessitating sophisticated approaches to manage complex hierarchies spanning cache systems, tightly coupled memory, and external DRAM controllers. Contemporary embedded AI processors must balance competing demands of access latency, bandwidth capacity, power consumption, and deterministic timing constraints across heterogeneous computing architectures integrating CPU cores, graphics processors, and specialized accelerators. Energy disparities between on-chip and off-chip memory access drive optimization strategies, including hierarchical data reuse, cache-aware tensor placement, and deterministic memory allocation schemes. Model quantization techniques reduce memory footprint and bandwidth requirements while maintaining inference accuracy, enabling deployment of large neural networks on resource-constrained platforms. Quality-of-service arbitration mechanisms and advanced memory controller configurations prevent bandwidth starvation in multi-agent systems executing concurrent AI workloads. The synthesis of cache optimization, tightly coupled memory utilization, external memory controller tuning, quantization strategies, and zero-copy data flow establishes a comprehensive framework for embedded AI system design, enabling substantial throughput improvements and power consumption reductions while meeting real-time safety-critical requirements.