For the state-of-the-art convolutional neural networks (CNNs), the accuracy of the inference is increased by raising the number of the layer. However, it can cause the higher computation complexities of the CNNs. The 3D CNN accelerator is applied to solve the computation complexities for the CNNs. However, the 3D CNN accelerator will encounter the serious thermal problem. In this article, we propose the predicted rate-controlled and task-allocated dynamic thermal management (prctaDTM) for the 3D CNN accelerator systems. The proposed prctaDTM contains the rate-controlled temperature prediction (RCTP), workload movement (WM), and workload adjustment (WA) to control the workloads of the 3D CNN accelerator and adjust the parallelism for the 3D CNN accelerator. The goal is to solve the thermal problem of the 3D CNN accelerator. In our experiments, the latency for the 3D CNN accelerator can be reduced by 18.1%–40.1% and the peak temperature for the 3D accelerator can be controlled close to the predefined thermal limitation. Moreover, the prediction errors with RCTP can be constrained below 0.66˚C for different CNN applications.

The regular topology of mesh-based network-on-chip (NoC) provides flexible and scalable architecture for chip multiprocessor (CMP) systems. However, as the complexity of network increases, routing problems become performance bottlenecks. In the field of wide area networks (WANs), ant colony optimization (ACO) has been applied to an adaptive routing for improving performance and achieving load balancing. Nevertheless, if we directly apply ACO to NoC systems, the implementation cost of ACO is excessively high. To overcome this problem, the ACO-based adaptive routing must be reformulated while considering both router cost and NoC efficiency. This work proposes the regional ACO-based cascaded adaptive routing (RACO-CAR) scheme with the following techniques: 1) table elimination by removing redundant information, 2) table sharing by grouping pheromone information to merge table content, and 3) cascaded routing that assigns traffic to different uncongested regions to balance traffic. Our experimental results demonstrate that the RACO-CAR scheme has an improvement of 3.9-36.84 percent in saturation throughput compared with existing adaptive routing schemes. The implementation cost of the RACO-CAR router is only 37.4 percent of that of the ACO-based router with full routing table. Therefore, the proposed RACO-CAR scheme has high area efficiency, defined as saturation throughput divided by the total cost of router.

Yuen-Hsien Tseng, Lung-Hao Lee, Shu-Yen Lin, Bo-Shun Liao, Mei-Jun Liu, Hsin-Hsi Chen, Oren Etzioni, Anthony Fader. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers. 2014.

Network-on-chip systems can achieve higher performance than bus systems for chip multiprocessor systems. However, as the complexity of the network increases, the channel and switch congestion problems become major performance bottlenecks. An effective adaptive routing algorithm can help minimize path congestion through load balancing. However, conventional adaptive routing schemes only use channel-based information to detect the congestion status. Due to the lack of switch-based information, channel-based information is difficult to reveal the real congestion status along the routing path. Therefore, in this paper, we remodel the path congestion information to show hidden spatial congestion information and improve the effectiveness of routing path selection. We propose a path-congestion-aware adaptive routing (PCAR) scheme based on the following techniques: 1) a path-congestion-aware selection strategy that simultaneously considers switch congestion and channel congestion, and 2) a contention prediction technique that uses the rate of change in the buffer level to predict possible switch contention. The experimental results show that the proposed PCAR scheme can achieve a high saturation throughput with an improvement of 15.4%-48.7% compared to existing routing schemes. The proposed PCAR method also includes a VLSI architecture, which has higher area efficiency with an improvement of 16%-35.7% compared with the other router designs.

DRAM renovation calls for a holistic architecture exploration to cope with bandwidth growth and latency reduction need. In this paper, we present DRAM area power timing (DArT), a DRAM area, power, and timing modeling tool, for array assembly and interface customization. Through proper design abstraction, our component-based modeling approach provides increased flexibility and higher accuracy, making DArT suitable for DRAM architecture exploration and performance estimation. We validate the accuracy of DArT with respect to the physical layout and circuit simulation of an industrial 68 nm commodity DRAM device as a reference. The experiment results show that the maximum deviations from the reference design, in terms of area, timing, and power, are 3.2%, 4.92%, and 1.73%, respectively. For an architectural projection by porting it to a 45 nm process, the maximum deviations are 3.4%, 3.42%, and 8.57%, respectively. The combination of modeling performance, flexibility, and accuracy of DArT allows us to easily explore new DRAM architectures in the future, including 3-D stacked DRAM.

Three-dimensional network-on-chip (3D NoC) has been proposed to solve the complex on-chip communication issues in future 3D multicore systems. However, the thermal problems of 3D NoC are more serious than 2D NoC due to chip stacking. To keep the temperature below a certain thermal limit, the thermal emergent routers are usually throttled. Then, the topology of 3D NoC becomes a Nonstationary Irregular Mesh (NSI-Mesh). To ensure the successful packet delivery in the NSI-Mesh, some routing algorithms had been proposed in the previous works. However, the network still suffers from extremely traffic imbalance among lateral and vertical logic layer. In this paper, we propose a Topology Aware Adaptive Routing (TAAR) to balance the traffic load for NSI-Mesh in 3D NoC. TAAR has three routing modes, which can be dynamically adjusted based on the topology status of the routing path. In addition to increasing routing flexibility, the TAAR also increases both vertical and lateral path diversity to balance the traffic load. Compared with the related adaptive routing methods, the experimental results show that the proposed TAAR can reduce 19 to 295 percent traffic loads in the bottom logic layer and improve around 7.7 to 380 percent network throughput. According to our proposed VLSI architecture, the TAAR only needs less than 24.8 percent hardware overhead compared with the previous works.

In this paper, we proposed a scalable built-in self-test/self-diagnosis architecture, surrounding test ring (STR), to detect and locate faulty FIFOs and faulty MUXs for 2D-mesh based CMP systems. Proposed STR supports 97.79% fault coverage in FIFOs and MUXs and tests with 388 ~ 2886 test cycles in different testing methods and mesh sizes.

On-chip networks (OCNs) have been proposed to solve the increasing scale and complexity of the designs in nanoscale multicore VLSI designs. The concept of irregular meshes is an important issue because IPs of different sizes may be supported by various vendors. In order to solve routing problems in irregular meshes, modified routing algorithms to detour oversized IPs (OIPs) are needed. However, directly applying fault-tolerant routing algorithms may cause two serious problems: 1) heavy traffic loads around OIPs and 2) unbalanced traffic loads in irregular meshes. In this paper, we propose an OIP avoidance prerouting (OAPR) algorithm to solve the aforementioned problems. The proposed OAPR can make traffic loads evenly spread on the networks and shorten the average paths of packets. Therefore, the networks using the OAPR have lower latency and higher throughput than those using fault- tolerant routing algorithms. In our experiments, four different cases are simulated to demonstrate that the proposed OAPR improves 13.3 percent to 100 percent sustainable throughputs than two previous fault-tolerant routing algorithms. Moreover, the hardware overhead of the OAPR is less than 1 percent compared to the cost of a whole router. Hence, the proposed OAPR algorithm has good performance and is practical for irregular mesh-based OCNs.