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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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Mechanical Efficiency of Real Machines01:14

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The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Power Efficient Machine Learning Models Deployment on Edge IoT Devices.

Anastasios Fanariotis1, Theofanis Orphanoudakis1, Konstantinos Kotrotsios1

  • 1Digital Systems and Media Computing Lab, School of Sciences and Technology, Hellenic Open University, 26334 Patras, Greece.

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Summary

Machine learning on edge devices, including Internet-of-Thing (IoT) devices, is limited by resource scarcity. This study evaluates the power efficiency of ML optimization techniques on embedded systems, comparing results to idle power consumption.

Keywords:
autonomous systemsedge computingmachine learningneural networks compressionpower efficiency

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Artificial Intelligence

Background:

  • The evolution of computing towards ubiquitous and pervasive systems relies on resource-constrained edge and Internet-of-Thing (IoT) devices.
  • Machine Learning (ML) algorithms enable Artificial Intelligence (AI) inference on these devices, but their limited computational power and energy autonomy pose significant challenges.
  • Existing research often focuses on ML model optimization and compression, potentially overlooking critical power consumption and efficiency implications.

Purpose of the Study:

  • To experimentally evaluate the power efficiency of established ML optimization and compression techniques applied to embedded systems.
  • To analyze the real-world performance and energy implications of these optimizations on devices with scarce resources.
  • To compare the power efficiency of different system architectures when running optimized ML models.

Main Methods:

  • Application of well-known ML optimization and compression methods to selected ML models.
  • Experimental measurement of power consumption during ML inference on two distinct embedded systems.
  • Comparison of power efficiency metrics against the baseline idle power consumption of each system.

Main Results:

  • Quantified power efficiency gains from applying ML optimization techniques on resource-constrained edge and IoT devices.
  • Demonstrated trade-offs between computational performance and power consumption for different optimization strategies.
  • Identified architectural differences influencing the power efficiency of ML inference on embedded systems.

Conclusions:

  • ML optimization techniques can significantly impact the power efficiency of edge and IoT devices, but careful consideration of implementation is crucial.
  • The choice of system architecture plays a vital role in the overall power efficiency of AI inference on embedded platforms.
  • This research provides valuable insights for developing energy-efficient AI solutions for ubiquitous computing environments.