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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