High bandwidth 3-D-stacked dynamic random access memory (DRAM) has been proposed to address the memory wall in modern systems, especially when it is used as a large last-level cache (LLC). However, stacking DRAM directly on top of the processor significantly impedes the efficiency of cooling, potentially causing thermal issues both in the processor and DRAM. Dynamic thermal management (DTM) based on DRAM temperature can be heavily intrusive because the normal working temperature for DRAM is lower than the processor temperature limit. This paper shows that in many cases it is better to disable hot portions of the cache rather than apply DTM and slow down the processor. Three temperature-aware cache management mechanisms are proposed to decrease the performance impact of DTM on 3-D systems. Our experiments show these techniques can improve the performance of DRAM-targeted DTM by 26.1% on average which make 3-D systems more practical for the future high-performance computing.

System reliability is a first-class concern as technology continues to shrink, resulting in increased vulnerability to traditional sources of errors such as single event upsets. By tracking access counts and the Architectural Vulnerability Factor (AVF), application data can be partitioned into groups based on how frequently it is accessed (its "hotness") and its likelihood to cause program execution error (its "risk"). This is particularly useful for memory systems which exhibit heterogeneity in their performance and reliability such as Heterogeneous Memory Architectures – with a typical configuration combining slow, highly reliable memory with faster, less reliable memory. This work demonstrates that current state of the art, performance-focused data placement techniques affect reliability adversely. It shows that page risk is not necessarily correlated with its hotness; this makes it possible to identify pages that are both hot and low risk, enabling page placement strategies that can find a good balance of performance and reliability. This work explores heuristics to identify and monitor both hotness and risk at run-time, and further proposes static, dynamic, and program annotation-based reliability-aware data placement techniques. This enables an architect to choose among available memories with diverse performance and reliability characteristics. The proposed heuristic-based reliability-aware data placement improves reliability by a factor of 1.6x compared to performance-focused static placement while limiting the performance degradation to 1%. A dynamic reliability-aware migration scheme, which does not require prior knowledge about the application, improves reliability by a factor of 1.5x on average while limiting the performance loss to 4.9%. Finally, program annotation-based data placement improves the reliability by 1.3x at a performance cost of 1.1%.

In the near future, die-stacked DRAM will be increasingly present in conjunction with off-chip memories in hybrid memory systems. Research on this subject revolves around using the stacked memory as a cache or as part of a flat address space. This paper proposes MemPod, a scalable and efficient memory management mechanism for flat address space hybrid memories. MemPod monitors memory activity and periodically migrates the most frequently accessed memory pages to the faster on-chip memory. MemPod's partitioned architectural organization allows for efficient scaling with memory system capabilities. Further, a big data analytics algorithm is adapted to develop an efficient, low-cost activity tracking technique. MemPod improves the average main memory access time of multi-programmed workloads, by up to 29% (9% on average) compared to the state of the art, and that will increase as the differential between memory speeds widens. MemPod's novel activity tracking approach leads to significant cost reduction (~12800x lower storage space requirements) and improved future prediction accuracy over prior work which maintains a separate counter per page.

With current DRAM technology reaching its limit, emerging heterogeneous memory systems have become attractive to continue scaling memory performance. This paper argues for using a small, fast memory closer to the processor as part of a flat address space where the memory system is composed of two or more memory types. OS-transparent management of such memory has been proposed in prior works such as CAMEO and Part of Memory (PoM). Data migration is typically handled either at coarse granularity with high bandwidth overheads (as in PoM) or at fine granularity with low hit rate (as in CAMEO). Prior work uses restricted address mapping from only congruence groups in order to simplify the mapping. At any time, only one page (block) from a congruence group is resident in the fast memory. In this paper, we present a flat address space organization called SILC-FM that uses large granularity but allows subblocks from two pages to coexist in an interleaved fashion in fast memory. Data movement is done at subblocked granularity, avoiding fetching of useless subblocks and consuming less bandwidth compared to migrating the entire large block. SILC-FM can achieve more spatial locality hits than CAMEO and PoM due to page-level operation and interleaving blocks respectively. The interleaved subblock placement improves performance by 55% on average over a static placement scheme without data migration. We also selectively lock hot blocks to prevent them from being involved in hardware swapping operations. Additional features such as locking, associativity and bandwidth balancing improve performance by 11%, 8%, and 8% respectively, resulting in a total of 82% performance improvement over a no migration static placement scheme. Compared to the best state-of-the-art scheme, SILC-FM gets performance improvement of 36% with 13% energy savings.

Networks-on-Chip (NoC) are becoming increasingly susceptible to emerging reliability threats. The need to detect and localize the occurrence of faults at runtime is steadily becoming imperative. In this work, we propose NoCAlert , a comprehensive online and real-time fault detection and localization mechanism that demonstrates 0% false negatives within the interconnect for the fault models and stimulus set used in this study. Based on the concept of invariance checking, NoCAlert employs a group of lightweight microchecker modules that collectively implement real-time hardware assertions. The checkers operate concurrently with normal NoC operation, thus eliminating the need for periodic, or triggered-based, self-testing. Based on the pattern/signature of asserted checkers, NoCAlert can pinpoint the location of the fault at various granularity levels. Most important, 97% of the transient and 90% of the permanent faults are detected instantaneously, within a single clock cycle upon fault manifestation. The fault localization accuracy ranges from 90% to 100%, depending on the desired localization granularity. Extensive cycle-accurate simulations in a 64-node CMP and analysis at the RTL netlist-level demonstrate the efficacy of the proposed technique.

We propose a power-efficient many-core server-on-chip system with 3D-stacked Wide I/O DRAM targeting cloud workloads in datacenters. The integration of 3D-stacked Wide I/O DRAM on top of a logic die increases available memory bandwidth by using dense and fast Through-Silicon Vias (TSVs) instead of off-chip IOs, enabling faster data transfers at much lower energy per bit. We demonstrate a methodology that includes full-system microarchitectural modeling and rapid virtual physical prototyping with emphasis on the thermal analysis. Our findings show that while executing CPU-centric benchmarks (e.g. SPECInt and Dhrystone), the temperature in the server-on-chip (logic+DRAM) is in the range of 175-200°C at a power consumption of less than 20W, exceeding the reliable operating bounds without any cooling solutions, even with embedded cores. However, with real cloud workloads, the power density in the server-on-chip remains much below the temperatures reached by the CPU-centric workloads as a result of much lower power burnt by memory-intensive cloud workloads. We show that such a server-on-chip system is feasible with a low-cost passive heat sink eliminating the need for a high-cost active heat sink with an attached fan, creating an opportunity for overall cost and energy savings in datacenters.

The widespread proliferation of the Chip Multi-Processor (CMP) paradigm has cemented the criticality of the on-chip interconnection fabric. The Network-on-Chip (NoC) is becoming increasingly susceptible to emerging reliability threats. As technology feature sizes diminish into the nanoscale regime, reliability and process variability artifacts within the NoC start to become prominent. The need to detect the occurrence of faults at run-time is steadily becoming imperative. In this work, we propose NoCAlert, a comprehensive on-line and real-time fault detection mechanism that demonstrates 0% false negatives within the interconnect, for the fault model and stimulus set used in this study. Based on the concept of invariance checking, NoCAlert employs a group of lightweight micro-checker modules that collectively implement real-time hardware assertions. The checkers operate seamlessly and concurrently with normal NoC operation, thus eliminating the need for periodic, or triggered-based, self-testing. More importantly, 97% of the faults are detected instantaneously. Extensive cycle-accurate simulations in a 64-node CMP demonstrate the efficacy of the proposed technique. Finally, hardware synthesis results using commercial 65 nm technology libraries indicate minimal area and power overhead of 3% and less than 1%, respectively, and negligible impact on the router's critical path.