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Multi-core heterogeneous architectures are playing the important role in server, mobile and all commercial devices. With the advent of internet of things in today's applications and increase in the workloads inputs, predictive simulating and computing tools are mandatory for the effective implementation of the multi-core heterogeneous architectures for the different applications. Many tools such as MacPACT, ESEC has been into existence but an intelligent computing framework tool for the predictive selection of the cores depending on the workloads remains in the darker side of the research. Hence the new computing framework called VEERBENCH has been proposed which works on the learning and training mechanisms for the usage of the cores in the heterogeneous architectures depending on the workloads. The framework uses the fuzzy clustering with the extreme learning machines and formulation of adaptive and cognitive energy (FACE) rule sets which are used for the energy and performance-based allocation of the cores. The proposed knowledge-based test bench has been compared with the other tools such as MACPACT, ESEC and with the other energy-based scheduler benchmarks and the obtained results are shown.

The development of blockchain has led to the emergence and widespread use of decentralized cryptocurrencies around the globe. As of 2021, the global market capitalization of cryptocurrencies has crossed two trillion dollars. With increasing popularity and adoption, investors have begun to see cryptocurrencies as an alternative to conventional financial assets. However, the volatility associated with cryptocurrencies makes them a highly risky investment. This gives rise to the need for accurate and efficient price prediction models which can help reduce risks associated with cryptocurrency investments. The model aims at predicting the price of two popular cryptocurrencies: Bitcoin and Ethereum. Tensor processing unit (TPU) is used for providing a distributed environment for the proposed model. The results show that the distributed TPU-trained model performed significantly better than the conventional CPU-trained model in terms of training time while maintaining a high degree of accuracy.

In the present era, energy is progressively turning into the major limitation in designing multicore chips. However, power and performance are the primary segments of energy, which are contrarily correlated in multicore architectures. This research primarily focused on optimizing energy level of multicore chips using parallel workloads by utilizing either power or execution advancement based on machine learning computation on dynamic programming. To do as such, the novel dynamic machine learning‐based heuristic energy optimization (DML‐HEO) algorithm has been designed and developed in this research on application‐specific controllers to optimize energy‐level on multicore architecture. Here DML‐HEO is implemented on the controller to maximize the execution inside a fixed power spending plan or to limit the expended capacity to accomplish a similar pattern execution. The controller is additionally scalable as it does not bring about critical overhead due to the increase in quantity of cores. The strategy has been assessed utilizing controllers on a full‐framework test system at lab‐scale analysis. The experimental results demonstrate that our proposed DML‐HEO system shows improving performance than the traditional system.