Exhaustive verification techniques do not scale with the complexity of today’s multi-tile Multi-processor Systems-on-chip (MPSoCs). Hence, runtime verification (RV) has emerged as a complementary method, which verifies the correct behavior of applications executed on the MPSoC during runtime. In this article, we propose a decentralized monitoring architecture for large-scale multi-tile MPSoCs. In order to minimize performance and power overhead for RV, we propose a lightweight and non-intrusive hardware solution. It features a new specialized tracing interconnect that distributes and sorts detected events according to their timestamps. Each tile monitor has a consistent view on a globally sorted trace of events on which the behavior of the target application can be verified using logical and timing requirements. Furthermore, we propose an integer linear programming-based algorithm for the assignment of requirements to monitors to exploit the local resources best. The monitoring architecture is demonstrated for a four-tiled MPSoC with 20 cores implemented on a Virtex-7 field-programmable gate array (FPGA).

No abstract

The continuous technology scaling of integrated circuits results in increasingly higher power densities and operating temperatures. Hence, modern many-core processors require sophisticated thermal and resource management strategies to mitigate these undesirable side effects. A simulation-based evaluation of these strategies is limited by the accuracy of the underlying processor model and the simulation speed. Therefore, we present, for the first time, an FPGA-based evaluation approach to test and compare thermal and resource management strategies using the combination of benchmark generation, FPGA-based ASIC emulation and run-time monitoring. The proposed benchmark generation method enables an evaluation of run-time management strategies for applications with various run-time characteristics. Furthermore, the ASIC emulation platform features a novel distributed temperature emulator design, whose overhead scales linearly with the number of integrated cores, and a novel dynamic voltage frequency scaling emulator design, which precisely models the timing and energy overhead of voltage and frequency transitions. In our evaluations, we demonstrate the proposed approach for a tiled many-core processor with 80 cores on four Virtex-7 FPGAs. Additionally, we present the suitability of the platform to evaluate state-of-the-art run-time management techniques with a case study.

In many Multi-Processor Systems-on-Chip (MPSoCs), traffic between cores is unbalanced. This motivates the use of an application-specific Network-on-Chip (NoC) that is customized and can provide a high performance at low cost in terms of power and area. However, finding an optimized application-specific NoC architecture is a challenging task due to the huge design space. This article proposes to apply machine learning approaches for this task. Using graph rewriting, the NoC Design Space Exploration (DSE) is modelled as a Markov Decision Process (MDP). Monte Carlo Tree Search (MCTS), a technique from reinforcement learning, is used as search heuristic. Our experimental results show that—with the same cost function and exploration budget—MCTS finds superior NoC architectures compared to Simulated Annealing (SA) and a Genetic Algorithm (GA). However, the NoC DSE process suffers from the high computation time due to expensive cycle-accurate SystemC simulations for latency estimation. This article therefore additionally proposes to replace latency simulation by fast latency estimation using a Recurrent Neural Network (RNN). The designed RNN is sufficiently general for latency estimation on arbitrary NoC architectures. Our experiments show that compared to SystemC simulation, the RNN-based latency estimation offers a similar speed-up as the widely used Queuing Theory (QT). Yet, in terms of estimation accuracy and fidelity, the RNN is superior to QT, especially for high-traffic scenarios. When replacing SystemC simulations with the RNN estimation, the obtained solution quality decreases only slightly, whereas it suffers significantly when QT is used.