COMRANCE: A rapid method for Network-on-Chip design space exploration

A regression-based design space exploration methodology is proposed that models the impacts of the memory hierarchy and the network-on-chip (NoC) on the overall chip multiprocessor (CMP) performance. Designers cannot explore all possible designs for a NoC without considering interactions with the rest of the uncore, in particular the cache configuration and memory hierarchy which determine the amount and pattern of the traffic on the NoC. The proposed regression model is able to capture the salient design points of the uncore for a comprehensive design space exploration by designing memory and NoC-specific regression models and leveraging recent advances in uncore simulation. To show the utility of our methodology, Uncore RPD, two case studies are presented: i) analyzing and refining regression models for an 8-core CMP and ii) performing a rapid design space exploration to find best performing designs of a NoC-based CMP given area-constraints for CMPs of up to 64 cores. Through these case studies, it is shown that i) simultaneous consideration of the memory and NoC parameters in the NoC design space exploration can refine uncore-based regression models, ii) sampling techniques must consider the dynamic design space of the uncore, and iii) overall, the proposed regression models reduce the amount of simulations required to characterize the NoC design space by up to four orders of magnitude.

Design space exploration (DSE) has become a notoriously difficult problem due to the exponentially increasing size of design space of microprocessors and time-consuming simulations. To address this issue, machine learning techniques have been widely employed to build predictive models. However, most previous approaches randomly sample the training set leading to considerable simulation cost and low prediction accuracy. In this paper, we propose an efficient and precise DSE methodology by combining statistical sampling and Adaboost learning technique. The proposed method includes three phases. (1) Firstly, orthogonal design based feature selection is employed to prune design space. (2) Sencondly, an orthogonal array based training data sampling method is introduced to select the representative configurations for simulation. (3) Finally, a new active learning approach ActBoost is proposed to build predictive model. Evaluations demonstrate that the proposed framework is more efficient and precise than state-of-art DSE techniques.

Due to the large number of optimizations provided in modern compilers and to compiler optimization specific opportunities, a Design Space Exploration (DSE) is necessary to search for the best sequence of compiler optimizations for a given code fragment (e.g., function). As this exploration is a complex and time consuming task, in this paper we present DSE strategies to select optimization sequences to both improve the performance of each function and reduce the exploration time. The DSE is based on a clustering approach which groups functions with similarities and then explore the reduced search space provided by the optimizations previously suggested for the functions in each group. The identification of similarities between functions uses a data mining method which is applied to a symbolic code representation of the source code. The DSE process uses the reduced set identified by clustering in two ways: as the design space or as the initial configuration. In both ways, the adoption of a pre-selection based on clustering allows the use of simple and fast DSE algorithms. Our experiments for evaluating the effectiveness of the proposed approach address the exploration of compiler optimization sequences considering 49 compilation passes and targeting a Xilinx MicroBlaze processor, and were performed aiming performance improvements for 41 functions. Experimental results reveal that the use of our new clustering-based DSE approach achieved a significant reduction on the total exploration time of the search space (18x over a Genetic Algorithm approach for DSE) at the same time that important performance speedups (43% over the baseline) were obtained by the optimized codes.

The demand of high-performance, scalable and energy-efficient on-chip interconnection network are growing in many-core processors. However, the limitations of the metallic interconnect make electronic Network-on-Chip inefficient. Photonic on-chip networks with advanced CMOS-compatible photonic devices, brings a bright future. In this paper, we present a hierarchical butterfly-based photonic Network-on-Chip, with hybrid router, its topology, and the communication protocol. The proposed network leverages the electronic and photonic technology to improve on-chip network performance. HBPNoC uses hierarchical architecture for packet switching in intra-cluster networks and circuit switching in the inter-cluster network. A butterfly-based structure, comprised of 2×2 photonic switching elements, is used for inter-cluster communication. The simulation results show that our proposed network has very good performance with low optical loss.

With complexities of systems-on-chips (SoCs) rising almost daily, the system designers have been searching for new design methodology that can handle given complexities with decreased times-to-market. The obvious solution that comes to mind is increasing the level of abstraction. However the system designers also care about the system architectures, HW/SW performance, and communication protocols. Furthermore, the transaction-level model (TLM) can satisfy the requests on complex design with relative high simulation speed and well performance evaluation. In this paper, we implement a TLM-based network-on-chip (NoC) platform and share-bus system architecture with SystemC. We also implement the H.263 encoder as the system application, and apply a design methodology at electronic-system level (ESL) to make design modeling, design space exploration and performance evaluation. Out platform is able to evaluate performance in relatively short time, obtain important information and complete the design more instinctively. In addition, we compare and contrast the NoC and share-bus system architectures in terms of evaluation performance. In experimental result, the performance bottleneck in communication congestion is solved well by using the NoC instead of using the share-bus design.

During the last few decades an unprecedented technological growth has been at the center of the embedded systems design paramount, with Moore’s Law being the leading factor of this trend. Today in fact an ever increasing number of cores can be integrated on the same die, marking the transition from state-of-the-art multi-core chips to the new many-core design paradigm. Despite the extraordinarily high computing power, the complexity of many-core chips opens the door to several challenges. As a result of the increased silicon density of modern Systems-on-a-Chip (SoC), the design space exploration needed to find the best design has exploded and hardware designers are in fact facing the problem of a huge design space. Virtual Platforms have always been used to enable hardware-software co-design, but today they are facing with the huge complexity of both hardware and software systems. In this thesis two different research works on Virtual Platforms are presented: the first one is intended for the hardware developer, to easily allow complex cycle accurate simulations of many-core SoCs. The second work exploits the parallel computing power of off-the-shelf General Purpose Graphics Processing Units (GPGPUs), with the goal of an increased simulation speed. The term Virtualization can be used in the context of many-core systems not only to refer to the aforementioned hardware emulation tools (Virtual Platforms), but also for two other main purposes: 1) to help the programmer to achieve the maximum possible performance of an application, by hiding the complexity of the underlying hardware. 2) to efficiently exploit the high parallel hardware of many-core chips in environments with multiple active Virtual Machines. This thesis is focused on virtualization techniques with the goal to mitigate, and overtake when possible, some of the challenges introduced by the many-core design paradigm.

In this paper, we will study the on-chip network and memory hierarchy design of the Godson-T - a homogeneous many-core processor. Godson-T has 64 cores (with private L1 cache), and 16 global L2 cache banks. All these on-chip units are connected by a 2D 8 × 8 mesh network. Our study reveals that:(a) Global on-chip L2 cache can effectively alleviate the memory pressure caused by the data-thirsty on-chip computing engines. However, its potential is still limited by both the off-chip and the in-chip bandwidth, especially when increasing the number of active threads.(b) On-chip traffic congestion is largely caused by the intensive memory access requests issued from the on-chipcores. Therefore, the design of the on-chip network must consider the available performance of the datapath that connects the processor to the main memory. (c) In theory, different applications have different communication patterns (Berkeley's view). However, the application's runtime communication pattern is only determined by the design of the underlying memory hierarchy and on-chip interconnection. These conclusions are generally applicable to a wide variety of many-core processors with similar design.

Network-on-Chip (NoC) based multiprocessors have become popular as a scalable alternative to classical bus architectures. The performance evaluation of NoC-based multiprocessors is largely based on simulation. However, precise simulation is extremely slow. Additionally, there are many design parameters that affect the total performance. Therefore, it is practically impossible to use the precise simulation for the design space exploration purposes. To alleviate this problem, prototyping NoC systems and estimating their performances are critically important. In this paper, we present a generalized novel performance model that combined with the simulations for designing NoC-based multiprocessors. We revealed that the performance impact of cache and network latencies are dominant. Moreover, network congestion rarely happens under near appropriate configuration. Thus, the performance model is mainly constructed using the hardware parameters and the statistics that obtained from a simple cache simulation that is separated from the network behavior. The proposed performance model is used not only to obtain fast and accurate performance, but also to guide the NoC-based multiprocessor design space exploration. The accuracy of our approach and its practical use are illustrated through simulation. The results showed that proposed model can estimate performance with only 3.4% error on average and 21% at worst. We also confirmed that our evaluation framework can estimate 360 times faster than the brute force full system simulation.

Multi-core systems-on-chip are becoming state-of-the-art. Therefore, there is a need for a fast and energy-efficient interconnect to take full advantage of the computational capabilities. Integration of silicon photonics with a traditional electrical interconnect in a Network-on-Chip (NoC) proposes a promising solution for overcoming the scalability issues of electrical interconnect. In this article, we derive and evaluate prediction modeling techniques for the design space exploration (DSE) of application-specific communication architectures for an Optical Network-on-Chip (ONoC). Our proposed model accurately predicts network packet latency, contention delay, and the static and dynamic energy consumption of the network. This work specifically addresses the challenge of accurately estimating performance metrics of the entire design space without having to perform time-consuming and computationally intensive exhaustive simulations. The proposed technique, based on machine learning (ML), can build accurate prediction models using only 10% to 50% (best case and worst case) of the entire design space. The accuracy, expressed as R 2 (Coefficient of Determination) is 0.99901, 0.99967, 0.99996, and 0.99999 for network packet latency, contention delay, static energy consumption, and dynamic energy consumption, respectively, in six different benchmarks from the Splash-2 benchmark suite, chosen among 6 different machine learning prediction models.

A typical design space exploration flow involves an event-based simulator in the loop, often leading to an actual evaluation time that can exceed practical limits for realistic applications. Chip multi-processor architectures further exacerbate this problem given that the actual simulation speed decreases by increasing the number of cores of the chip. Traditional design space exploration lacks of efficient techniques that reduce the number of architectural alternatives to be analyzed. In this chapter, we introduce a set of statistical and machine learning techniques that can be used to predict system level metrics by using closed-form analytical expressions instead of lengthy simulations; the latter are called Response Surface Models (RSM). The principle of RSM is to exploit a set of simulations generated by one or more Design of Experiments strategies to build a surrogate model to predict the system-level metrics. The response model has the same input and output features of the original simulation based model but offers significant speed-up by leveraging analytical, closed-form functions which are tuned during model training. The techniques presented in this chapter can be used to improve the performance of traditional design space exploration algorithms such as those presented in Chap. 3.

Network on Chip (NoC) has been proposed as a new paradigm for designing System on Chip (SoC) which supports high degree of scalability and reusability. One of the most important issues in an NoC design is how to map an application on NoC-based architecture in order to satisfy the performance and cost requirements. In this paper a novel procedure is introduced to find an optimal application-specific NoC using Particle Swarm Optimization (PSO) and a linear function which considers communication cost, robustness index and contention factor. Communication cost is a common metric in evaluation of different mapping algorithms which have direct impact on power consumption and performance of the mapped NoC. Robustness index is used as a criterion for estimating fault-tolerant properties of NoCs and contention factor highly affects the latency, throughput and communication energy consumption. The experimental results on two real core graphs VOPD and MPEG-4 reveal the power of proposed procedure to explore design space and how effective designer can customize and prioritize the impact of metrics.

Network-on-chip facilitates glueless interconnection of various on-chip components in the forthcoming system-on-chips. As in the case of any new technology, security is a major concern in network-on-chip (NoC) design too. In this work, we explore a covert threat model for multiprocessor system-on-chips (MPSoCs) stemming from the use of malicious third-party network-on-chips (NoCs). We illustrate that a rogue NoC (rNoC) can selectively disrupt the perceived availability of on-chip resources, thereby causing large performance bottlenecks for the applications running on the MPSoC platform. Further, to counter the threat posed by rNoC, we propose a runtime latency auditor that enables an MPSoC integrator to monitor the trustworthiness of the deployed NoC throughout the chip lifetime. We also discuss measures that can be taken to minimize the impact of a rNoC, once it is detected. Our comprehensive cross-layer analysis of our novel detection technique indicates modest overheads of 12.73% in area, 9.844% in power, and 5.4% in terms of network latency.

Contemporary advances in neural networks (NNs) have demonstrated their potential in different applications such as in image classification, object detection or natural language processing. In particular, reconfigurable accelerators have been widely used for the acceleration of NNs due to their reconfigurability and efficiency in specific application instances. To determine the configuration of the accelerator, it is necessary to conduct design space exploration to optimize the performance. However, the process of design space exploration is time consuming because of the slow performance evaluation for different configurations. Therefore, there is a demand for an accurate and fast performance prediction method to speed up design space exploration. This work introduces a novel method for fast and accurate estimation of different metrics that are of importance when performing design space exploration. The method is based on a Gaussian process regression model parametrised by the features of the accelerator and the target NN to be accelerated. We evaluate the proposed method together with other popular machine learning based methods in estimating the latency and energy consumption of our implemented accelerator on two different hardware platforms targeting convolutional neural networks. We demonstrate improvements in estimation accuracy, without the need for significant implementation effort or tuning.

Network-on-Chip (NoC) is a critical hardware supporting on-chip data movement in multicore and many-core processors. Data movement is predicted to become a major power-consuming operation compared to computation as the technology scales. This paper shows that unifying router power gating with data placement significantly reduces dynamic and static power needed for moving data in NoC for memory hierarchy. Region-based data placement enables to localize private data traffic and to concentrate shared data traffic in one region of NoC, which shapes traffic in a well-behaved way and increases power gating opportunities. In this regard, a dimensionally power-gated router with a region-based routing algorithm is proposed to reduce router static power and performance/energy overheads in power gating. Full-system evaluation using SPEComp benchmarks shows that the dynamic power gating management achieves NoC power savings by 46% and the static power gating management improves energy-efficiency by 20%.

Customized network-oriented communication architectures have recently become a must to support high bandwidth SoCs. To this end, a corresponding communication design flow is necessary to support the design space exploration of complex SoCs with tight design constraints. In order to exploit the benefits introduced by the NoC approach for the on-chip communication, the paper presents a Pareto Simulated Annealing (PSA) approach for the customization of the network topology. The proposed PSA approach has been applied to STNoC, the Network on-Chip developed by STMicroelectronics. Starting from the ring topology, the proposed application-specific design flow tries to find a set of customized topologies (optimized in terms of performance and area/energy overhead) by adding custom links up to the spidergon topology.