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Resistors In Series01:10

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A resistor is an ohmic device that limits the flow of charge in a circuit. Most circuits have more than one resistor. If several resistors are connected together and connected to a battery, the current supplied by the battery depends on the equivalent resistance of the circuit. The equivalent resistance of a combination of resistors depends on both their individual values and how they are connected. The simplest combination of resistors is the series combination. 
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Electrical devices in any circuit can be connected either by series or parallel connections. Additionally, circuits can be connected involving both of these connections, known as combination or complex circuits. As these circuits have complex resistor connections, it is necessary to identify different parts as either series or parallel connections, then the whole combination of series and parallel resistors can be reduced to a single equivalent resistance. With the known equivalent resistance...
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Resistors are in parallel when one end of all the resistors are connected to a continuous wire of negligible resistance and the other end of all the resistors are also connected to one another through a continuous wire of negligible resistance. In the case of a parallel configuration, the potential drop across each resistor is the same. Current through each resistor can be found using Ohm’s law, I = V/R, where the voltage is constant across each resistor. The sum of the individual currents...
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Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
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An alternating emf or voltage source is needed to supply an alternating current (AC) to a circuit. A coil of wire rotating in a magnetic field at a constant angular speed represents such a source. It also generates a sinusoidal alternating emf and serves as an industrial alternator.
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The universe is composed of matter in different forms, and all forms of matter contain energy.  The different forms of energy on Earth originate from the Sun — the ultimate energy source. Plants capture light energy from the Sun, and, via the process of photosynthesis, convert it into chemical energy. This stored energy from plants can be harnessed in many ways. For example, eating plant products as food provides energy for our body to function, and burning wood or coal (fossilized...
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Synaptic Resistors for Concurrent Inference and Learning with High Energy Efficiency.

Cameron D Danesh1, Christopher M Shaffer1, Dhruva Nathan1

  • 1Department of Mechanical and Aerospace Engineering, California NanoSystems Institute, University of California, Los Angeles, CA, 90095, USA.

Advanced Materials (Deerfield Beach, Fla.)
|March 26, 2019
PubMed
Summary
This summary is machine-generated.

Researchers developed novel synaptic resistors that mimic the human brain's energy efficiency for processing big data. These artificial synapses enable faster, more efficient analog computing, outperforming supercomputers.

Keywords:
carbon nanotubeconcurrent inference and learninghigh energy efficiencyparallelismsynaptic resistor

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Area of Science:

  • Neuromorphic Engineering
  • Materials Science
  • Computer Science

Background:

  • Current supercomputers like Summit are energy-inefficient compared to the human brain.
  • Digital computing's serial processing limits efficient handling of big data.
  • Existing electronic devices lack synaptic functions for concurrent learning and inference.

Purpose of the Study:

  • To develop electronic components that emulate brain-like synaptic functions.
  • To overcome the limitations of conventional digital computing for big data processing.
  • To achieve high energy efficiency in artificial intelligence systems.

Main Methods:

  • Fabrication and characterization of novel synaptic resistors.
  • Design of a synaptic resistor circuit for analog signal processing.
  • Implementation of speech inference and learning using the developed circuit.

Main Results:

  • Synaptic resistors successfully emulated analog signal processing, correlative learning, and nonvolatile memory.
  • The synaptic resistor circuit achieved an energy efficiency of ≈1.6 × 1017 FLOPS W-1.
  • Concurrent speech inference and learning were performed in parallel analog mode.

Conclusions:

  • Synaptic resistors offer a pathway to brain-like computational efficiency.
  • This technology circumvents fundamental limitations of current computer architectures.
  • Scaled-up circuits promise real-time, high-efficiency inference and learning from big data.