Data scheduling and placement in deep learning accelerator

Mirmahaleh, Seyedeh Yasaman Hosseini; Reshadi, Midia; Bagherzadeh, Nader; Khademzadeh, Ahmad

doi:10.1007/s10586-021-03355-8

Cited by 2 publications

(3 citation statements)

References 46 publications

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“…Additionally, Figure 8a,b show improving communication delay and energy efficiency by approximately 6.15% and 10.26% for assumption 2 compared with assumption 1 after detecting defective nodes. After detecting the defective nodes and simulating them by the cycle-accurate tool, the relationship between processing elements demonstrates reducing the total hopcouns for assumption 2 compared with assumption 1, which leads to performance improvement [19,20,39]. This section analyzed the astrocyte operations and their relationship to neuron cells and proved the dependency between cholesterol distribution and the activity of neurons.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The network size depends on the total number of tasks, which are mapped onto the nodes based on the pattern of the relationship between astrocyte, neuron, and dendrite. To simulate a mesh network, this work uses a cycle-accurate SystemC-based simulator and describes the relationship between the network nodes as the traffic tables and importing them to the simulator [19,20,39]. To deploy the simulator with a multicasting traffic feature, the researchers inspired the Noxim tool [40,41].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…According to the special and successful operation of astrocyte cells to distribute cholesterol into the neurons and its phagocytosis against the waste cells, the work utilizes the astrocyte pattern to IoMT traffic distribution onto mesh network's PEs and fault detection onto them. Nevertheless, the type of network-on-chip (NoC) based communication infrastructure, traffic pattern, flow mapping and data or bus scheduling method, and clustering the network nodes affect improving or deteriorating the ideal performance [19,20,39].…”

Section: Astrocyte-flow Mapping Onto Mesh Network Nodesmentioning

confidence: 99%

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An Astrocyte-Flow Mapping on a Mesh-Based Communication Infrastructure to Defective Neurons Phagocytosis

et al. 2021

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In deploying the Internet of Things (IoT) and Internet of Medical Things (IoMT)-based applications and infrastructures, the researchers faced many sensors and their output’s values, which have transferred between service requesters and servers. Some case studies addressed the different methods and technologies, including machine learning algorithms, deep learning accelerators, Processing-In-Memory (PIM), and neuromorphic computing (NC) approaches to support the data processing complexity and communication between IoMT nodes. With inspiring human brain structure, some researchers tackled the challenges of rising IoT- and IoMT-based applications and neural structures’ simulation. A defective device has destructive effects on the performance and cost of the applications, and their detection is challenging for a communication infrastructure with many devices. We inspired astrocyte cells to map the flow (AFM) of the Internet of Medical Things onto mesh network processing elements (PEs), and detect the defective devices based on a phagocytosis model. This study focuses on an astrocyte’s cholesterol distribution into neurons and presents an algorithm that utilizes its pattern to distribute IoMT’s dataflow and detect the defective devices. We researched Alzheimer’s symptoms to understand astrocyte and phagocytosis functions against the disease and employ the vaccination COVID-19 dataset to define a set of task graphs. The study improves total runtime and energy by approximately 60.85% and 52.38% after implementing AFM, compared with before astrocyte-flow mapping, which helps IoMT’s infrastructure developers to provide healthcare services to the requesters with minimal cost and high accuracy.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Astrocyte-flow Mapping Onto Mesh Network Nodesmentioning

confidence: 99%

See 1 more Smart Citation

An Astrocyte-Flow Mapping on a Mesh-Based Communication Infrastructure to Defective Neurons Phagocytosis

et al. 2021

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Quasi-Mapping and Satisfying IoT Availability with a Penalty-Based Algorithm

et al. 2021

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The Internet of things and medical things (IoT) and (IoMT) technologies have been deployed to simplify humanity’s life, which the complexity of communications between their layers was increased by rising joining the applications to IoT and IoMT-based infrastructures. The issue is challenging for decision-making and the quality of service where some researchers addressed the reward-based methods to tackle the problems by employing reinforcement learning (RL) algorithms and deep neural networks (DNNs). Nevertheless, satisfying its availability remains a challenge for the quality of service due to the lack of imposing a penalty to the defective devices after detecting faults. This paper proposes a quasi-mapping method to transfer the roles of sensors and services onto a neural network’s nodes to satisfy IoT-based applications’ availability using a penalty-backwarding approach into the NN’s weights and prunes weak neurons and synaptic weights (SWs). We reward the sensors and fog services, and the connection weights between them when are covered the defective nodes’ output. Additionally, this work provides a decision-making approach to dedicate the suitable service to the requester using employing a threshold value in the NN’s output layer according to the application. By providing an intelligent algorithm, the study decides to provide a service based on its availability and updating initial information, including faulty devices and new joined components. The observations and results prove decision-making accuracy for different IoT-based applications by approximately 95.8–97% without imposing the cost. The study reduces energy consumption and delay by approximately 64.71% and 47.4% compared without using neural networks besides creating service availability. This idea affects deploying IoT infrastructures to decision-making about providing appropriate services in critical situations because of removing defective devices and joining new components by imposing penalties and rewards by the designer, respectively.

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Data scheduling and placement in deep learning accelerator

Cited by 2 publications

References 46 publications

An Astrocyte-Flow Mapping on a Mesh-Based Communication Infrastructure to Defective Neurons Phagocytosis

An Astrocyte-Flow Mapping on a Mesh-Based Communication Infrastructure to Defective Neurons Phagocytosis

Quasi-Mapping and Satisfying IoT Availability with a Penalty-Based Algorithm

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