3D Network-on-Chip Research Articles

3D network-on-chip data acquisition system mapping based on reinforcement learning and improved attention mechanism

The three-dimensional Network-on-Chip (NoC) data acquisition system is designed to create a time-interleaved data acquisition system using NoC technology. In the design of NoC application systems, optimizing the mapping algorithm can effectively reduce network communication latency. Aiming at the mapping challenge of a large number of functional IP nodes in 3D NoC data acquisition system, the reinforcement learning and improved attention mechanism mapping algorithm (RA-Map) is proposed. The RA-Map mapping algorithm employs node function encoding and node position encoding to express the properties of an IP node in the task graph preprocessing. The local attention mechanism is used in the mapping network encoder, and the fusion of dynamic key node information is proposed in the decoder. The mapping result evaluation network achieves unsupervised training of the mapping network. These targeted improvements improve the quality of the mapping. Experimental results show that the RA-Map mapping algorithm can effectively model the IP core mapping. Compared with the DPSO algorithm and SA algorithm, the average communication cost of RA-Map mapping algorithm is reduced by 6.5 % and 8.5 %, respectively.

IWO-IGA—A Hybrid Whale Optimization Algorithm Featuring Improved Genetic Characteristics for Mapping Real-Time Applications onto 2D Network on Chip

Network on Chip (NoC) has emerged as a potential substitute for the communication model in modern computer systems with extensive integration. Among the numerous design challenges, application mapping on the NoC system poses one of the most complex and demanding optimization problems. In this research, we propose a hybrid improved whale optimization algorithm with enhanced genetic properties (IWOA-IGA) to optimally map real-time applications onto the 2D NoC Platform. The IWOA-IGA is a novel approach combining an improved whale optimization algorithm with the ability of a refined genetic algorithm to optimally map application tasks. A comprehensive comparison is performed between the proposed method and other state-of-the-art algorithms through rigorous analysis. The evaluation consists of real-time applications, benchmarks, and a collection of arbitrarily scaled and procedurally generated large-task graphs. The proposed IWOA-IGA indicates an average improvement in power reduction, improved energy consumption, and latency over state-of-the-art algorithms. Performance based on the Convergence Factor, which assesses the algorithm’s efficiency in achieving better convergence after running for a specific number of iterations over other efficiently developed techniques, is introduced in this research work. These results demonstrate the algorithm’s superior convergence performance when applied to real-world and synthetic task graphs. Our research findings spotlight the superior performance of hybrid improved whale optimization integrated with enhanced GA features, emphasizing its potential for application mapping in NoC-based systems.

An enhanced approach towards improving the performance of embedding memory management units into Network-on-Chip

Network-on-Chip (NoC) initiates the design procedure of interconnection network into SoC - System-on-Chip. The current technique overcomes the drawbacks of traditional bus-based SoC, for instance, poor scalability, small-link bandwidth, and large delay. The network-on-Chip systems required minimum buffering space and low-latency requirements. Here, the FIFO buffer is utilized as a virtual channel to evade deadlock issues and thus enhances throughput. The current research focused on an enhanced toward improving Embedding-Memory-management- Units performance into Network-on-Chip. Here, the 3D NoC was considered in the study for Noc improvement and has achieved high performance. Further, A first-in-first-out (FIFO) buffer was used in NoC routers to store the data packets temporarily. Eventually, the RAM -random access memory was proposed that serves as a link between the crossbar switch and the input ports. The simulation result of the current research results in only 0 to 16 memory for data storage in a stack out of 64, with the almost empty signal being high.

OECS: a deep convolutional neural network accelerator based on a 3D hybrid optical-electrical NoC

Various emerging technologies have been proposed and applied to deep convolutional neural networks (DCNNs) to enhance their execution efficiency and accuracy while reducing the number of parameters. However, previous dataflow-based DCNN accelerators have primarily focused on accelerating the convolution (CONV) layer while reducing the overhead of data movement through specific data reuse strategies to improve performance. When deploying these emerging DCNNs in conventional accelerators, frequent data access from both the external memory and internal processing units of the accelerator can result in significant data movement overhead, leading to decreased acceleration performance. To address these challenges, this paper proposes a novel three-dimensional hybrid optical-electrical network-on-chip (NoC) accelerator called the optical-electronic channel-stationary system (OECS). OECS leverages a channel stationary (CS) calculation mode and stay-at-local data storage strategy to minimize the movement and processing of all data in the local processing element (PE). This strategy reduces data movement between each PE in the accelerator and between the accelerator and external memory, resulting in improved acceleration performance. Simulation results demonstrate that, compared to state-of-the-art accelerators, OECS achieves a 53% improvement in execution speed and saves approximately 44% of energy consumption associated with data movement.

Design and Implementation of High QoS 3D-NoC using Modified Double Particle Swarm Optimization on FPGA

One technique to overcome the exponential growth bottleneck is to increase the number of cores on a processor, although having too many cores might cause issues including chip overheating and communication blockage. The problem of the communication bottleneck on the chip is presently effectively resolved by networks-on-chip (NoC). A 3D stack of chips is now possible, thanks to recent developments in IC manufacturing techniques, enabling to reduce of chip area while increasing chip throughput and reducing power consumption. The automated process associated with mapping applications to form three-dimensional NoC architectures is a significant new path in 3D NoC research. This work proposes a 3D NoC partitioning approach that can identify the 3D NoC region that has to be mapped. A double particle swarm optimization (DPSO) inspired algorithmic technique, which may combine the characteristics having neighbourhood search and genetic architectures, also addresses the challenge of a particle swarm algorithm descending into local optimal solutions. Experimental evidence supports the claim that this hybrid optimization algorithm based on Double Particle Swarm Optimisation outperforms the conventional heuristic technique in terms of output rate and loss in energy. The findings demonstrate that in a network of the same size, the newly introduced router delivers the lowest loss on the longest path. Three factors, namely energy, latency or delay, and throughput, are compared between the suggested 3D mesh ONoC and its 2D version. When comparing power consumption between 3D ONoC and its electronic and 2D equivalents, which both have 512 IP cores, it may save roughly 79.9% of the energy used by the electronic counterpart and 24.3% of the energy used by the latter. The network efficiency of the 3D mesh ONoC is simulated by DPSO in a variety of configurations. The outcomes also demonstrate an increase in performance over the 2D ONoC. As a flexible communication solution, Network-On-Chips (NoCs) have been frequently employed in the development of multiprocessor system-on-chips (MPSoCs). By outsourcing their communication activities, NoCs permit on-chip Intellectual Property (IP) cores to communicate with one another and function at a better level. The important components in assigning application duties, distributing the work to the IPs, and coordinating communication among them are mapping and scheduling methods. This study aims to present an entirely advanced form of research in the area of 3D NoC mapping and scheduling applications, grouping the results according to various parameters and offering several suggestions for further research.

Hybrid Optimization Algorithm Based on Double Particle Swarm in 3D NoC Mapping

Increasing the number of cores on a chip is one way to solve the bottleneck of exponential growth but an excessive number of cores can lead to problems such as communication blockage and overheating of the chip. Currently, networks-on-chip (NoC) can offer an effective solution to the problem of the communication bottleneck within the chip. With current advancements in IC manufacturing technology, chips can now be 3D-stacked in order to increase chip throughput as well as reduce power consumption while reducing the area of the chip. Automating the mapping of applications into 3D NoC topologies is an important new direction for research in the field of 3D NoC. In this paper, a 3D NoC partitioning algorithm is proposed, which can delineate the 3D NoC region to be mapped. Additionally, a double particle swarm optimization (DPSO) based heuristic algorithm is proposed, which can integrate the characteristics of neighborhood search and genetic algorithms, and thus solve the problem of a particle swarm algorithm falling into local optimal solutions. It is experimentally demonstrated that this DPSO-based hybrid optimization algorithm has a higher throughput rate and lower energy loss than the traditional heuristic algorithm.

TB-NUCA: A Temperature-Balanced 3D NUCA Based on Bayesian Optimization

Three-dimensional network-on-chip (NoC) is the primary interconnection method for 3D-stacked multicore processors due to their excellent scalability and interconnect flexibility. With the support of 3D NoC, 3D non-uniform cache architecture (NUCA) is commonly used to organize the last-level cache (LLC) due to its high capacity and fast access latency. However, owing to the layered structure that leads to longer heat dissipation paths and variable inter-layer cooling efficiency, 3D NoC experiences a severe thermal problem that has a big impact on the reliability and performance of the chip. In traditional memory-to-LLC mapping in 3D NUCA, the traffic load in each node is inconsistent with its heat dissipation capability, causing thermal hotspots. To solve the above problem, we propose a temperature-balanced NUCA mapping mechanism named TB-NUCA. First, the Bayesian optimization algorithm is used to calculate the probability distribution of cache blocks in each node in order to equalize the node temperature. Secondly, the structure of TB-NUCA is designed. Finally, comparative experiments were conducted under random, transpose-2, and shuffle traffic patterns. The experimental results reveal that, compared with the classical NUCA mapping mechanism (S-NUCA), TB-NUCA can increase the mean-time-to-failure (MTTF) of routers by up to 28.13% while reducing the maximum temperature, average temperature, and standard deviation of temperature by a maximum of 4.92%, 4.48%, and 20.46%, respectively.

Multicriteria Deming Regressive African Buffalo Optimized Mapping for 3D NoC Architecture Design

Purpose. Networks-on-Chip (NoC) is a network-based communication between operating cores and intellectual property (IP) cores integrated on the same chip. An efficient design of NoC ensures high-speed data transfer and minimum essential connections in large-scale multicore, low power applications. Design/methodology/approach. A unique technique called Multicriteria Deming Regressive African Buffalo Optimized Mapping Weighted Directive Graph Theory (MDRABOMWDGT) is introduced for an efficient Network-on-Chip architecture design. The main aim of the proposed technique is to find efficient operating cores integrated on the chip with minimum time. Initially, a set of IP cores are listed with their connections from the benchmark dataset. Then Multicriteria Deming Regressive African Buffalo Optimization is applied to the topology for mapping strategy on 3D NoC, with communication metrics such as throughput, latency, and computation time. The optimization technique initializes the population of cores in search space. For each core in the network, communication metrics are measured; then Deming regression is applied to analyze multicriteria functions for minimizing computation time. Further, fitness is measured to get an optimal IP core for improving the performance of mapping in 3D NoC. Findings. Comprehensive experimental evaluation is conducted using a benchmark dataset, communication metrics are measured, and results show significant improvement in performance with respect to energy parameters compared to state-of-the-art works. Originality/value. The mapping strategy for 3D NOC is developed and the results are compared with the state-of-the-art techniques.

3D NoC Low-Power Mapping Optimization Based on Improved Genetic Algorithm.

Power optimization is an important part of network-on-chip(NoC) design. This paper proposes an improved algorithm based on genetic algorithm on how to properly map IP (Intellectual Property) cores to 3D NoC. First, in view of the randomness of the traditional genetic algorithm in individual selection, an improved greedy algorithm is used in the initial population generation stage to make the generated individuals reach the optimal. Secondly, in view of the weak local optimization ability of the traditional genetic algorithm and prone to premature problems, the simulated annealing algorithm is added in the crossover operation stage to make the offspring reach the global optimum. The experimental results show that compared with the traditional genetic algorithm, the algorithm has better convergence and low power consumption performance, which can quickly search for a better solution, in the case of a large number of cores (124 IP cores), the average power consumption can be reduced by 42.2%.

Software-based online self-testing using bounded model checking for 3D network-on-chips

Online testing has become essential for the reliable operations of manycore systems using three-dimension (3D) network-on-chip (NoC) fabric. This paper presents a software-based online self-testing solution using bounded model checking (BMC) for 3D NoCs. The proposed method implements BMC on the extended finite state machine of the 3D NoC and extracts leading sequences to triggerinternal functions. Next, it detects structural defects in each function activated by the leader sequence using constrained ATPG. Finally, it effectively organizes packets to test the 3D NoC and reduce test cost. Experimental results demonstrate that the proposed method achieves high fault coverage online with extremely lower test cost and outperforms existing methods.

Traffic aware routing in 3D NoC using interleaved asymmetric edge routers

Network-on-Chip (NoC) has emerged as a cost-effective on-chip interconnects solution for Tiled Chip Multi Processors (TCMP) where many computational cores occupy a single chip. The performance of NoC network can be greatly enhanced by incorporating 3D IC technology formed by stacking several active NoC layers using Through Silicon Via (TSV) vertical interconnections. 3D NoC routers improve network throughput and have minimal latency at the cost of increased router area and power dissipation. Performance degradation can occur in 3D structures due to unequal traffic distribution across the chip leading to larger power density and larger on-chip temperature that affect system reliability. In this paper, we come up with an interleaved vertical edge routing design approach in 3D NoC that employs asymmetrical routing algorithm and uses a unique flit prioritization unit for improving performance of bufferless mesh NoC. Experimental results indicate that our proposed router has better network performance with minimal hardware overhead when compared with conventional bufferless networks, engaging same number of routers.

Q-Function-Based Traffic- and Thermal-Aware Adaptive Routing for 3D Network-on-Chip

Die-stacking technology is expanding the space diversity of on-chip communications by leveraging through-silicon-via (TSV) integration and wafer bonding. The 3D network-on-chip (NoC), a combination of die-stacking technology and systematic on-chip communication infrastructure, suffers from increased thermal density and unbalanced heat dissipation across multi-stacked layers, significantly affecting chip performance and reliability. Recent studies have focused on runtime thermal management (RTM) techniques for improving the heat distribution balance, but performance degradations, owing to RTM mechanisms and unbalanced inter-layer traffic distributions, remain unresolved. In this study, we present a Q-function-based traffic- and thermal-aware adaptive routing algorithm, utilizing a reinforcement machine learning technique that gradually incorporates updated information into an RTM-based 3D NoC routing path. The proposed algorithm initially collects deadlock-free directions, based on the RTM and topology information. Subsequently, Q-learning-based decision making (through the learning of regional traffic information) is deployed for performance improvement with more balanced inter-layer traffic. The simulation results show that the proposed routing algorithm can improve throughput by 14.0%–28.2%, with a 24.9% more balanced inter-layer traffic load and a 30.6% more distributed inter-layer thermal dissipation on average, compared with those obtained in previous studies of a 3D NoC with an 8 × 8 × 4 mesh topology.

On Runtime Communication and Thermal-Aware Application Mapping and Defragmentation in 3D NoC Systems

Many-core systems connected by 3D Networks-on-Chip (NoC) are emerging as a promising computation engine for systems like cloud computing servers, big data systems, etc . Mapping applications at runtime to 3D NoCs is the key to maintain high throughput of the overall chip under a thermal/power constraint. However, the goals of optimizing both the communication latency and chip peak temperature are contradicting due to several reasons. First, exploiting the vertical TSV links can accelerate communications, while low peak temperature prefers that the tasks to be mapped closer to the heat sink, instead of using the vertical links. Second, mapping tasks in close proximity can reduce communication latency, but at the cost of poor heat dissipation. To address these issues, in this paper, we propose an efficient runtime mapping algorithm to reduce both communication latency and overall application running time under thermal constraint. In essence, this algorithm first selects a 3D cuboid core region of a specific shape for each incoming application by setting the region's number of occupied vertical layers and its distance to the heat sink, in order to optimize its communication performance and peak temperature. Next, the exact locations of the core regions in the chip are determined, followed by a task-to-core mapping. A defragmentation algorithm is also proposed to keep free core regions contiguous. The experimental results have confirmed that, compared to two recently proposed runtime mapping algorithms, our proposed approach can reduce the total running time by up to 48% and communication cost by up to 44%, with a low runtime overhead.

A new congestion-aware routing algorithm in network-on-chip: 2D and 3D comparison

Network on-Chip (NoC) is scalable, flexible, modular communication structure for Multi/Many-core architectures. It allows simpler interconnect models with higher bandwidth compared to bus-based architectures. When a message is exchanged inside an NoC, it passes through several routers. When the communication load is high, contention and congestion may appear in central routers, and lead to an increased latency and power consumption. 3D NoC has been proposed as a solution to improve the performances, especially: throughput, latency, and energy efficiency. In this paper, we first report the results of theoretical and practical comparison between 2D and 3D mesh Network-on-Chip topology. We focus on communication supports, especially routing, in the goal of reducing latency and avoiding congestion. Further, we propose a novel exhaustive routing algorithm to avoid and reduce congestion in 2D and 3D NoC. Also, we discuss the implementation of the proposed algorithm in both topologies and the trade-off between their overheads and their performances. We demonstrate the effectiveness of our algorithm using a wide set of synthetic experiments using NOXIM simulator.

A thermal-sensitive design of a 3D torus-based optical NoC architecture

In order to overcome limitations of traditional electronic interconnects in terms of power efficiency and bandwidth density, optical networks-on-chip (NoC) based on silicon photonics have been proposed as an emerging on-chip communication architecture for chip multiprocessors (CMPs) with large core counts. However, due to thermo-optic effects, wavelength-selective silicon photonic devices such as microresonators suffer from temperature-dependent wavelength shift. In this work, we propose a thermal-sensitive design of a 3D torus-based optical NoC architecture. For the 3D torus-based optical NoC architecture, we propose a hybrid optical-electronic router architecture with a fully-connected 7 × 7 optical switching fabric. Besides, a thermal-sensitive routing algorithm is proposed to optimize the optical power loss in the presence of on-chip temperature variations. Simulation results show that for a set of synthetic traffic patterns, the proposed 3D torus-based optical NoC with the thermal-sensitive routing reduces the average thermal-induced optical power loss by 14.3% and 17% respectively as compared to the matched 3D torus-based optical NoC and the 3D mesh-based optical NoC with the traditional XYZ routing. For a set of real applications, the proposed 3D torus-based optical NoC with the thermal-sensitive routing reduces the worst-case optical power loss by 7.9% and 14.6% respectively as compared to the matched 3D torus-based optical NoC and the 3D mesh-based optical NoC with the traditional XYZ routing.

Learning-Based Application-Agnostic 3D NoC Design for Heterogeneous Manycore Systems

The rising use of deep learning and other big-data algorithms has led to an increasing demand for hardware platforms that are computationally powerful, yet energy-efficient. Due to the amount of data parallelism in these algorithms, high-performance 3D manycore platforms that incorporate both CPUs and GPUs present a promising direction. However, as systems use heterogeneity (e.g., a combination of CPUs, GPUs, and accelerators) to improve performance and efficiency, it becomes more pertinent to address the distinct and likely conflicting communication requirements (e.g., CPU memory access latency or GPU network throughput) that arise from such heterogeneity. Unfortunately, it is difficult to quickly explore the hardware design space and choose appropriate tradeoffs between these heterogeneous requirements. To address these challenges, we propose the design of a 3D Network-on-Chip (NoC) for heterogeneous manycore platforms that considers the appropriate design objectives for a 3D heterogeneous system and explores various tradeoffs using an efficient ML-based multi-objective optimization technique. The proposed design space exploration considers the various requirements of its heterogeneous components and generates a set of 3D NoC architectures that efficiently trades off these design objectives. Our findings show that by jointly considering these requirements (latency, throughput, temperature, and energy), we can achieve 9.6% better Energy-Delay Product on average at nearly iso-temperature conditions when compared to a thermally-optimized design for 3D heterogeneous NoCs. More importantly, our results suggest that our 3D NoCs optimized for a few applications can be generalized for unknown applications as well. Our results show that these generalized 3D NoCs only incur a 1.8% (36-tile system) and 1.1% (64-tile system) average performance loss compared to application-specific NoCs.

Extending BookSim2.0 and HotSpot6.0 for power, performance and thermal evaluation of 3D NoC architectures

With the increase in number and complexity of cores and components in Chip-Multiprocessors (CMP) and Systems-on-Chip (SoCs), a highly structured and efficient on-chip communication network is required to achieve high-performance and scalability. Network-on-Chip (NoC) has emerged as a reliable communication framework in CMPs and SoCs. Many 2-D NoC architectures have been proposed for efficient on-chip communication. Cycle accurate simulators model the functionality and behaviour of NoCs by considering micro-architectural parameters of the underlying components to estimate performance, power and energy characteristics. Employing NoCs in three-dimensional integrated circuits (3D-ICs) can further improve performance, energy efficiency, and scalability characteristics of 3D SoCs and CMPs. Minimal error estimation of energy and performance of NoC components is crucial in architecture trade-off studies. Accurate modeling of re:Horizontal and vertical links by considering micro-architectural and physical characteristics reduces the error in power and performance estimation of 3D NoCs. Additionally, mapping the temperature distribution in a 3D NoC reduces estimation error.This paper presents the 3D NoC modelling capabilities extended in two existing state-of-the-art simulators, viz., the 2D NoC Simulator - BookSim2.0 and the thermal behaviour simulator - HotSpot6.0. With the extended 3D NoC modules, the simulators can be used for power, performance and thermal measurements through micro-architectural and physical parameters. The major extensions incorporated in BookSim2.0 are: Through Silicon Via power and performance models, 3D topology construction modules, 3D Mesh topology construction using variable X, Y, Z radix, tailored routing modules for 3D NoCs. The major extensions incorporated in HotSpot6.0 are: parameterized 2D router floorplan, 3D router floorplan including Through Silicon Vias (TSVs), power and thermal distribution models of 2D and 3D routers.Using the extended 3D modules, performance (average network latency), and energy efficiency metrics (Energy-Delay Product) of variants of 3D Mesh and 3D Butterfly Fat Tree topologies have been evaluated using synthetic traffic patterns. Results show that the 4-layer 3D Mesh is 2.2 × better than 2-layer 3D Mesh and 4.5 × better than 3D BFT variants in terms of network latency. 3D Mesh variants have the lowest Energy Delay Product (EDP) compared to 3D BFT variants as there is an 80% reduction in link lengths and up to 3 × more TSVs. Another observation is that the EDP of the 4-layer 3D BFT (with transpose traffic) is 1.5 × the EDP of the 4-layer 3D Mesh (with transpose traffic). Further optimizations towards a tailored 3D BFT for transpose traffic could reduce this EDP gap with the 4-layer 3D Mesh. From the 3D NoC heat maps, it was found that the edge routers in the floorplan of the tested 3D Mesh and 3D BFT topologies have the least ambient temperature.

An Efficient Partitioning Algorithm Based on Hypergraph for 3D Network-On-Chip Architecture Floorplanning

Network-on-Chip (NoC) supplies a scalable and fast interconnect for the communication between the different IP cores in the System-on-Chip (SoC). With the growing complexity in consumer embedded systems, the emerging SoC architectures integrate more and more components for the different signal processing tasks. Two dimensional Network-on-Chip (2D NoC) becomes a bottleneck for the development of the SoC architecture because of its limitation on the area of chip and the long latency. In this case, SoC research is forcing on the exploration of three dimensions (3D) technology for developing the next generation of large SoC which integrates three dimensional Network-on-Chip (3D NoC) for the communication architecture. 3D design technology resolves the vertical inter-layer connection issue by Through-Silicon Vias (TSVs). However, TSVs occupy significant silicon area which limits the inter-layer links of the 3D NoC. Therefore, the task partitioning on 3D NoC must be judicious in large SoC design. In this paper, we propose an efficient layer-aware partitioning algorithm based on hypergraph (named ELAP-NoC) for the task partitioning with TSV minimization for 3D NoC architecture floorplanning. ELAP-NoC contains divergence stage and convergence stage. ELAP-NoC supplies firstly a multi-way min-cut partitioning to gradually divide a given design layer by layer in the divergence stage in order to get an initial solution, then this solution is refined in convergence stage. The experiments show that ELAP-NoC performs a better capacity in the partitioning of the different numbers of cores which supplies the first step for the 3D NoC floorplanning.

KBMA: A knowledge‐based multi‐objective application mapping approach for 3D NoC

Due to increased demands for communication at low power, an efficient application mapping has become vital in the area of network on chip (NoC). Optimisation of architectural structure in on-chip design is essential to maximise the performance of the network and minimise the cost functions. To address this issue, a knowledge-based memetic algorithm (KBMA) is proposed for 3D NoC for successful mapping with standard network topologies. The proposed KBMA adopts power, area and delay as a cost function for an effective mapping. The competence of the proposed method is verified through comparison with other natural inspired algorithms like particle swarm optimisation and genetic algorithm. The presented work is validated through four case studies which include real application benchmarks of NoC and random generated benchmarks using test graph for free.

Thermal-aware Test Scheduling Strategy for Network-on-Chip based Systems

Rapid progress in technology scaling has introduced massive parallel computing systems with multiple cores on the integrated circuit (IC), in which a flexible and scalable packet-switched architecture, Network-on-Chip (NoC), is commonly used for communication among the cores. However, technology scaling has also increased the susceptibility to internal defects in such systems. So, manufacturing tests of such multicore systems is crucial and this is a complex and time-consuming process. Due to stress on time-to-market, test engineers focus on the reduction of testtime and perform parallel tests of cores. Due to aggressive technology scaling into the nanometer regime, power consumption is also becoming a significant burden. Moreover, power consumption during manufacturing tests is more as compared to normal operation. In addition, peak power consumption is often significantly higher than the average power values. The consumed power leads to high temperature and creates hotspots, which in turn leads to failure of good parts, resulting in yield loss. Thermal safety during testing is an utmost challenging problem in NoC-based multicore systems, including three-dimensional NoC-based (3D NoC) multicore systems due to stacking of layers. This work proposes a preemptive test scheduling technique for NoC-based multicore systems to reduce the testtime by minimizing conflicts of resource usage. The preemptive test scheduling problem has been formulated using Integer Linear Programming (ILP). In this article, authors have also presented a thermal-aware test scheduling technique to test cores in 2D as well as 3D stacked NoC-based multicore systems using a Particle Swarm Optimization (PSO) based approach. To improve the solution further, several innovative augmentation techniques have been incorporated in the basic PSO. Experimental results highlight the effectiveness of the proposed method in reducing testtime and peak temperature under the power constraints and achieve a tradeoff between testtime and peak temperature.