The widening gap between the processor and memory performance has led to the inclusion of multiple levels of caches in the modern multi-core systems. Processors with simultaneous multithreading (SMT) support multiple hardware threads on the same physical core, which results in shared private caches. Any inefficiency in the cache hierarchy can negatively impact the system performance and motivates the need to perform a co-optimization of multiple cache levels by trading off individual application throughput for better system throughput and energy-delay-product (EDP). We propose a novel coordinated multiagent reinforcement learning technique for performing Dynamic Cache Co-partitioning, called Machine Learned Caches (MLC). MLC has low implementation overhead and does not require any special hardware data profilers. We have validated our proposal with 15 8-core workloads created using Spec2006 benchmarks and found it to be an effective co-partitioning technique. MLC exhibited system throughput and EDP improvements of up to 14% (gmean:9.35%) and 19.2% (gmean: 13.5%) respectively. We believe this is the first attempt at addressing the problem of multi-level cache co-partitioning.

Modern multi-core systems provide huge computational capabilities, which can be used to run multiple processes concurrently. To achieve the best possible performance within limited power budgets, the various system resources need to be allocated effectively. Any mismatch between runtime resource requirement and allocation leads to a sub-optimal energy-delay product (EDP). Different optimization techniques exist for addressing the problem of mismatch between the dynamic requirement and runtime allocation of the system resources. Choosing between multiple optimizations at runtime is complex due to the non-additive effects, making the scenario suitable for the application of machine learning techniques. We present a novel method, Machine Learned Machines (MLM), by using online reinforcement learning (RL) to perform dynamic partitioning of the last level cache (LLC), along with dynamic voltage and frequency scaling (DVFS) of the core and uncore (interconnection network and LLC). We have proposed and evaluated three different MLM co-optimization techniques based on independent and cooperative multi-agent learners. We show that the co-optimization results in a much lower system EDP than any of the techniques applied individually. We explore various RL models targeted toward optimization of different system metrics and study their effects on a system EDP, system throughput (STP), and Fairness. The various proposed techniques have been extensively evaluated with a mix of 20 workloads on a 4-core system using Spec2006 benchmarks. We have further evaluated our cooperative MLM techniques on a 16-core system. The results show an average of 20.5% and 19.1% system EDP improvement on a 4-core and 16-core system, respectively, with limited degradation of STP and Fairness.

The discrete wavelet transform (DWT) forms the core of the JPEG2000 image compression algorithm. JPEG2000 standard defines an irreversible DWT by a lifting scheme of factorized coefficients using (9, 7) Daubechies coefficients. The paper proposed optimizations on lifting based 1D-DWT data flow graph resulting in a new pipelining scheme, which is more power-efficient than existing approaches. The authors have shown that the constant multipliers defined by JPEG2000 standard, have latency of about 1.6 times the general adder latency. Hence, while finding the critical path, the basic assumption of multiplier latency being much greater than the adder latency does not hold for DWT in JPEG2000. How 75% multiplications can be reduced at the scaling step by this 2D-DWT implementation was also shown. This multiplication reduction not only saves power but also results in area saving by eliminating three multipliers in the hardware