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Scaling of CMOS feature size has long been a source of dramatic performance gains. However, the reduction in voltage levels has not been able to match this rate of scaling, leading to increasing operating temperatures and current densities. Given that most wearout mechanisms that plague semiconductor devices are highly dependent on these parameters, significantly higher failure rates are projected for future technology generations. Consequently, high reliability and fault tolerance, which have traditionally been subjects of interest for high-end server markets, are now getting emphasis in the mainstream desktop and embedded systems space. The popular solution for this has been the use of redundancy at a coarse granularity, such as dual/triple modular redundancy. In this work, we challenge the practice of coarse-granularity redundancy by identifying its inability to scale to high failure rate scenarios and investigating the advantages of finer-grained configurations. To this end, this paper presents and evaluates a highly reconfigurable multicore architecture, named StageNet (SN), that is designed with reliability as its first class design criteria. SN relies on a reconfigurable network of replicated processor pipeline stages to maximize the useful lifetime of a chip, gracefully degrading performance towards the end of life. Our results show that the proposed SN architecture can perform nearly 50% more cumulative work compared to a traditional multicore.

As manycores use dynamic energy ever more efficiently, static power consumption becomes a major concern. In particular, in a large manycore running at a low voltage, leakage in on-chip memory modules contributes substantially to the chip's power draw. This is unfortunate, given that, intuitively, the large multi-level cache hierarchy of a manycore is likely to contain a lot of useless data.An effective way to reduce this problem is to use a lowleakage technology such as embedded DRAM (eDRAM). However, eDRAM requires refresh. In this paper, we examine the opportunity of minimizing on-chip memory power further by intelligently refreshing on-chip eDRAM. We present Refrint, a simple approach to perform fine-grained, intelligent refresh of on-chip eDRAM multiprocessor cache hierarchies. We introduce the Refrint algorithms and microarchitecture. We evaluate Refrint in a simulated manycore running 16-threaded parallel applications. We show that an eDRAM-based memory hierarchy with Refrint consumes only 30% of the energy of a conventional SRAM-based memory hierarchy, and induces a slowdown of only 6%. In contrast, an eDRAM-based memory hierarchy without Refrint consumes 56% of the energy of the conventional memory hierarchy, inducing a slowdown of 25%.

Technology scaling has delivered on its promises of increasing device density on a single chip. However, the voltage scaling trend has failed to keep up, introducing tight power constraints on manufactured parts. In such a scenario, there is a need to incorporate energy-efficient processing resources that can enable more computation within the same power budget. Energy efficiency solutions in the past have typically relied on application specific hardware and accelerators. Unfortunately, these approaches do not extend to general purpose applications due to their irregular and diverse code base. Towards this end, we propose BERET, an energy-efficient co-processor that can be configured to benefit a wide range of applications. Our approach identifies recurring instruction sequences as phases of "temporal regularity" in a program's execution, and maps suitable ones to the BERET hardware, a three-stage pipeline with a bundled execution model. This judicious off-loading of program execution to a reduced-complexity hardware demonstrates significant savings on instruction fetch, decode and register file accesses energy. On average, BERET reduces energy consumption by a factor of 3-4X for the program regions selected across a range of general-purpose and media applications. The average energy savings for the entire application run was 35% over a single-issue in-order processor.

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EDRAM cells require periodic refresh, which ends up consuming substantial energy for large last-level caches. In practice, it is well known that different eDRAM cells can exhibit very different charge-retention properties. Unfortunately, current systems pessimistically assume worst-case retention times, and end up refreshing all the cells at a conservatively-high rate. In this paper, we propose an alternative approach. We use known facts about the factors that determine the retention properties of cells to build a new model of eDRAM retention times. The model is called Mosaic. The model shows that the retention times of cells in large eDRAM modules exhibit spatial correlation. Therefore, we logically divide the eDRAM module into regions or tiles, profile the retention properties of each tile, and program their refresh requirements in small counters in the cache controller. With this architecture, also called Mosaic, we refresh each tile at a different rate. The result is a 20x reduction in the number of refreshes in large eDRAM modules -practically eliminating refresh as a source of energy consumption.

Aggressive technology scaling provides designers with an ever increasing budget of cheaper and faster transistors. Unfortunately, this trend is accompanied by a decline in individual device reliability as transistors become increasingly susceptible to soft errors. We are quickly approaching a new era where resilience to soft errors is no longer a luxury that can be reserved for just processors in high-reliability, mission-critical domains. Even processors used in mainstream computing will soon require protection. However, due to tighter profit margins, reliable operation for these devices must come at little or no cost. This paper presents Shoestring, a minimally invasive software solution that provides high soft error coverage with very little overhead, enabling its deployment even in commodity processors with "shoestring" reliability budgets. Leveraging intelligent analysis at compile time, and exploiting low-cost, symptom-based error detection, Shoestring is able to focus its efforts on protecting statistically-vulnerable portions of program code. Shoestring effectively applies instruction duplication to protect only those segments of code that, when subjected to a soft error, are likely to result in user-visible faults without first exhibiting symptomatic behavior. Shoestring is able to recover from an additional 33.9% of soft errors that are undetected by a symptom-only approach, achieving an overall user-visible failure rate of 1.6%. This reliability improvement comes at a modest performance overhead of 15.8%.