Emerging applications like deep neural networks require high off-chip memory bandwidth and low dynamic loaded Double Data Rate SDRAM (DDR) latency. However, under the stringent physical constraints of chip packages and system boards, it is extremely expensive to further increase the bandwidth and reduce the dynamic loaded latency of off-chip memory in terms of DDR devices. To address the latency issues in DDR subsystems, this paper presents a novel architecture aiming at achieving latency optimization through a use case sensitive controller. We propose a reevaluation of conventional decoupling mechanisms and quasi-static arbitration methods in the DDR scheduling architecture. The adaptive scheduling algorithms offer significant advantages in various real-world scenarios. The research methodology involves implementing a rank-level timing aware read/write turnaround arbiter and setting read/write queue thresholds and read/write turnaround settings based on observed patterns. By implementing the arbiter and dynamically adjusting these parameters, the proposed architecture aims to optimize the performance of the DDR subsystem. To validate the effectiveness of the architecture, we conduct multiple experiments. These experiments evaluate the performance of the DDR subsystem under various workloads and configurations. The results demonstrate that the adaptive scheduling algorithms have advantages in achieving DDR performance attributes for workloads and improving system performance. The experimental results provide evidence of the architecture’s effectiveness in reducing latency by around 10% to 50% in various real-world scenarios.

To address the performance and power concerns in Double-Data-Rate SDRAM (DDR) subsystems, this paper presents an innovative method for the DDR memory controller scheduler. This design aims to strike a balance between power consumption and performance for the DDR subsystem. Our approach entails a critical reassessment of established mechanisms and the introduction of a quasi-static arbitration protocol for the DDR’s low-power mode (LPM) transition processes. Central to our proposed DDR power-management framework is the Low-Power-Management Engine (LPME), complemented by a suite of statistical algorithms tailored for implementation within the architecture. Our research strategy encompasses real-time monitoring of the DDR subsystem’s operational states, traffic intervals, and Quality of Service (QoS) metrics. By dynamically fine-tuning the DDR subsystem’s power-management protocols to transition in and out of identical power modes, our method promises substantial enhancements in both energy efficiency and operational performance across a spectrum of practical scenarios. To substantiate the efficacy of our proposed design, an array of experiments was conducted. These rigorous tests evaluated the DDR subsystem’s performance and energy consumption under a diverse set of workloads and system configurations. The findings are compelling: the LPME-driven architecture delivers significant power savings of over 41%, concurrently optimizing performance metrics like latency increase by no more than 22% in a high-performance operational context.