In recent years, inexact computing has been increasingly regarded as one of the most promising approaches for slashing energy consumption in many applications that can tolerate a certain degree of inaccuracy. Driven by the principle of trading tolerable amounts of application accuracy in return for significant resource savings-the energy consumed, the (critical path) delay, and the (silicon) area-this approach has been limited to application-specified integrated circuits (ASICs) so far. These ASIC realizations have a narrow application scope and are often rigid in their tolerance to inaccuracy, as currently designed; the latter often determining the extent of resource savings we would achieve. In this paper, we propose to improve the application scope, error resilience and the energy savings of inexact computing by combining it with hardware neural networks. These neural networks are fast emerging as popular candidate accelerators for future heterogeneous multicore platforms and have flexible error resilience limits owing to their ability to be trained. Our results in 65-nm technology demonstrate that the proposed inexact neural network accelerator could achieve 1.78-2.67× savings in energy consumption (with corresponding delay and area savings being 1.23 and 1.46×, respectively) when compared to the existing baseline neural network implementation, at the cost of a small accuracy loss (mean squared error increases from 0.14 to 0.20 on average).

Non-local adaptive routing techniques, which utilize statuses of both local and distant links to make routing decisions, have recently been shown to be effective solutions for promoting the performance of Network-on-Chip (NoC). The essence of non-local adaptive routing was an additional network dedicated to propagate congestion information of distant links on the NoC. While the dedicated Congestion Propagation Network (CPN) helps routers to make promising routing decisions, it incurs additional wiring and power costs and becomes an unnecessary decoration when the load of NoC is light. Moreover, the CPN has to be extended if one would utilize more sophisticated congestion information to enhance the performance of NoC, bringing in even larger wiring and power costs. This paper proposes an innovative non-local adaptive routing technique called FreeRider, which does not use a dedicated CPN but instead leverages free bits in head flits of existing packets to carry and propagate rich congestion information without introducing additional wires or flits. In order to balance the network load, FreeRider adopts a novel three-stage strategy of output link selection, which adequately utilizes the propagated information to make routing decisions. Experimental results on both synthetic traffic patterns and application traces show that FreeRider achieves better throughput, shorter latency, and smaller power consumption than a state-of-the-art adaptive routing technique with dedicated CPN.

A widely adopted methodology for verifying the memory subsystem of a Chip Multiprocessor (CMP) is to verify executions of parallel test programs on the CMP against the given memory consistency model, which has been long known to be time consuming in both theory and practice. To accelerate memory consistency verification, previous approaches have to bear the cost of availability (e.g., relying on dedicated hardware supports that have not been offered by many commodity CMPs) or completeness (e.g., missing some bugs). In the meantime, the impact of parallel programs on memory consistency verification has more or less been overlooked. One piece of evidence is that few investigations have been dedicated to finding appropriate test programs enabling more efficient verification From a novel perspective of test program, we devise a practical technique called “program regularization,” which can effectively reduce the computation time of memory consistency verification. The key intuition behind program regularization is that any parallel program, if being reformed appropriately, can enable efficient memory consistency verification. More specifically, for an original program, program regularization introduces some auxiliary memory addresses, and periodically inserts load/store operations accessing these addresses to the original program. With the regularized program, memory consistency verification can be accomplished in linear time (with respect to the number of memory operations) when the number of processors is fixed. Experimental results show that program regularization can significantly accelerate memory consistency verification. Last but not least, our technique, which does not rely on concrete verification algorithm or dedicated hardware support, can be smoothly integrated into existing presilicon/postsilicon verification platforms of industrial CMPs to speed up memory consistency verification.

The Godson-3 microprocessor aims at high-throughput server applications, high-performance scientific computing, and high-end embedded applications. It offers a scalable network on chip, hardware support for x86 emulation, and a reconfigurable architecture. The four-core Godson-3 chip is fabricated with 65-nm CMOS technology. Eight- and 16-core Godson-3 chips are in development.

In computer architecture research and development, simulation is a powerful way of acquiring and predicting processor behaviors. While architectural simulation has been extensively utilized for computer performance evaluation, design space exploration, and computer architecture assessment, it still suffers from the high computational costs in practice. Specifically, the total simulation time is determined by the simulator's raw speed and the total number of simulated instructions. The simulator's speed can be improved by enhanced simulation infrastructures (e.g., simulators with high-level abstraction, parallel simulators, and hardware-assisted simulators). Orthogonal to these work, recent studies also managed to significantly reduce the total number of simulated instructions with a slight loss of accuracy. Interestingly, we observe that most of these work are built upon statistical techniques. This survey presents a comprehensive review to such studies and proposes a taxonomy based on the sources of reduction. In addition to identifying the similarities and differences of state-of-the-art approaches, we further discuss insights gained from these studies as well as implications for future research.