In recent years, machine learning (ML) techniques are proven to be powerful tools in various emerging applications. Traditionally, ML techniques are processed on general-purpose CPUs and GPUs, but their energy efficiencies are limited due to their excessive support for flexibility. As an efficient alternative to CPUs/GPUs, hardware accelerators are still limited as they often accommodate only a single ML technique (family). However, different problems may require different ML techniques, which implies that such accelerators may achieve poor learning accuracy or even be ineffective. In this paper, we present a polyvalent accelerator architecture integrated with multiple processing cores, called ParaML, which accommodates ten representative ML techniques, including k-means, k-nearest neighbors (k-NN), naive Bayes (NB), support vector machine (SVM), linear regression (LR), classification tree (CT), deep neural network (DNN), learning vector quantization (LVQ), parzen window (PW), and principal component analysis (PCA). Benefited from our thorough analysis on computational primitives and locality properties of different ML techniques, the single-core ParaML can perform up to 1056 GOP/s (e.g., additions and multiplications) in an area of 3.51 mm2and consumes 596 mW only, estimated by ICC and PrimeTime PX with postsynthesis netlist, respectively. Compared with the NVIDIA K20M GPU (28-nm process), the single-core ParaML (65-nm process) is 1.21× faster, and can reduce the energy by 137.93×. We also compare the single-core ParaML with other accelerators. Compared with PRINS, single-core ParaML achieves 72.09× and 2.57× energy benefit for k-NN and k-means, respectively, and speeds up each query in k-NN by 44.76×. Compared with EIE, the single-core ParaML achieves 5.02× speedup and 4.97× energy benefit with 11.62× less area when evaluating with dense DNN. Compared with TPU, the single-core ParaML achieves 2.45× better power efficiency (5647 Gop/W versus 2300 Gop/W) with 321.36× less area. Compared to the single-core version, the 8-core ParaML will further improve the speedup up to 3.98× with an area of 13.44 mm2and a power of 2036 mW.

Large-scale graph processing is now a crucial task of many commercial applications, and it is conventionally supported by general-purpose processors. These processors are designed to flexibly support highly diverse workloads with classic techniques such as on-chip cache and dynamic pipelining. Yet, it is difficult for the on-chip cache to exploit irregular data locality in large-scale graph processing, even though there are a few high-degree vertices that are frequently accessed in real-world graphs, it is not efficient to perform regular arithmetic operations via sophisticated dynamic pipelining. In short, general-purpose processors could not be the ideal platforms to graph processing. In this paper, we design a reconfigurable graph processing accelerator, with the purpose of providing an energy-efficient and flexible hardware platform for large-scale graph processing. This accelerator features two main components, i.e., the on-chip storage to exploit the data locality of graph processing, and the reconfigurable functional units to adapt to diversified operations in different graph processing tasks. On a total of 36 practical graph processing tasks, we demonstrate that, on average, our accelerator design achieves 1.58x and 25.56x better performance and energy efficiency, respectively, than the GPU baseline.

Machine Learning (ML) techniques are pervasive tools in various emerging commercial applications, but have to be accommodated by powerful computer systems to process very large data. Although general-purpose CPUs and GPUs have provided straightforward solutions, their energy-efficiencies are limited due to their excessive supports for flexibility. Hardware accelerators may achieve better energy-efficiencies, but each accelerator often accommodates only a single ML technique (family). According to the famous No-Free-Lunch theorem in the ML domain, however, an ML technique performs well on a dataset may perform poorly on another dataset, which implies that such accelerator may sometimes lead to poor learning accuracy. Even if regardless of the learning accuracy, such accelerator can still become inapplicable simply because the concrete ML task is altered, or the user chooses another ML technique.

Debugging parallel programs is a well-known difficult problem. A promising method to facilitate debugging parallel programs is using hardware support to achieve deterministic replay on a Chip Multi-Processor (CMP). As a Design-For-Debug (DFD) feature, a practical hardware-assisted deterministic replay scheme should have low design and verification costs, as well as a small log size. To achieve these goals, we propose a novel and succinct hardware-assisted deterministic replay scheme named LReplay. The key innovation of LReplay is that instead of recording the logical time orders between instructions or instruction blocks as previous investigations, LReplay is built upon recording the pending period information infused by the global clock. By the recorded pending period information, about 99% execution orders are inferrable, implying that LReplay only needs to record directly the residual 1% noninferrable execution orders in production run. The 1% noninferrable orders can be addressed by a simple yet cost-effective direction prediction technique, which further reduces the log size of LReplay. Benefiting from the preceding innovations, the overall log size of LReplay over SPLASH-2 benchmarks is about 0.17B/K-Inst (byte per k-instruction) for the sequential consistency, and 0.57B/K-Inst for the Godson-3 consistency. Such log sizes are smaller in an order of magnitude than previous deterministic replay schemes incurring no performance loss. Furthermore, LReplay only consumes about 0.5% area of the Godson-3 CMP, since it requires only trivial modifications to existing components of Godson-3. The features of LReplay demonstrate the potential of integrating hardware support for deterministic replay into future industrial processors.