Neuromorphic Computing From Materials to Systems Architecture

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APPENDICES Acronyms 3D: Three-‐‐dimensional AER: Address Event Representation ALU: Arithmetic/Logic Unit BEOL: Back-‐‐end-‐‐of-‐‐line (BEOL) CMOS: Complementary Metal Oxide Semiconductor CNN: Convolutional Neural Network CPU: Central Processing Unit ColdRAM: Memory organized for infrequent access patterns DNN: Deep Neural Network DMA: Logic for supporting Direct Memory Access HotRAM: Memory organized for frequent access patterns InstRAM: Instruction Memory LIF: Leaky Integrate and Fire (neuron) LTPS: Long-‐‐Term Plasticity Synapses MLU: Machine Learning Unit, functional units optimized for ML operations MU: Memory Unit OutputRAM: Memory for holding output of operations STDP: Spike Time-‐‐Dependent Plasticity STPS: Short-‐‐Term Plasticity Synapses VLSI: Very Large-‐‐Scale Integration Neuromorphic Computing: From Materials to Systems Architecture 27
Glossary Axon: Transmitting signals to other neurons, axons provide long-‐‐distance output connection between a presynaptic to a postsynaptic neuron Charge trapping: Mobile electrons maybe trapped sometimes in defects invariably present in materials Crystalline-‐‐amorphous transition: Some materials change their physical structure from an ordered crystalline to a completely disordered (amorphous) phase; the electronic properties of these two phases maybe radically different Deep learning: Deep learning is the branch of machine learning that builds tools based on deep (multilayer) neural networks Dendrite: Providing multiple, distributed inputs into the neurons, the role of dendrites is commonly believed simply to provide signals from multiple neurons into a single neuron Depress: To increase resistance Domain wall motion: In some cases, a magnetic material reverts its magnetization by the motion of a separation (“wall”) in between two well-‐‐defined magnetization areas Filament formation: The resistance of two electrodes separated by an insulator may change drastically if a conducting filament forms; this can form because of intrinsic reasons or due to the motion of atoms in the insulator Hebbian learning: Change occurs in the synapse resistance after repeated spiking by the presynaptic neuron before the postsynaptic neuron; this is also sometimes referred to as spike time-‐‐dependent plasticity (STDP) Ionic motion: Certain solids and liquids ions may move under different driving forces such as high voltages, currents and temperature Learning: Conditioning; process by which the various elements of the system change and readjust depending on the type of stimuli they receive Machine learning: The branch of computer science that deals with building systems that can learn from and make predictions on data Magnetic tunnel junction: A device based on quantum mechanical tunneling that changes it resistance depending on the relative magnetization of the magnetic electrodes; the basis for most spintronics applications Neuromorphic Computing: From Materials to Systems Architecture 28
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Page 3 and 4: Contents EXECUTIVE SUMMARY .....
Page 5 and 6: 2. Creating a new computational
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Page 9 and 10: von Neumann Architecture Neuromo
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Page 15 and 16: IMPLEMENTATION NEEDS In this se
Page 17 and 18: nature of the connectivity of
Page 19 and 20: 3. Threshold behavior, in which
Page 21 and 22: Within each class, many materia
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Page 25 and 26: networks already implemented in
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Page 31 and 32: Neural networks digital hardware
Page 33 and 34: CATEGORY SYSTEM Oxides Phase Ch
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Neuromorphic Computing From Materials to Systems Architecture

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