6 research outputs found
Neuroinspired unsupervised learning and pruning with subquantum CBRAM arrays.
Resistive RAM crossbar arrays offer an attractive solution to minimize off-chip data transfer and parallelize on-chip computations for neural networks. Here, we report a hardware/software co-design approach based on low energy subquantum conductive bridging RAM (CBRAM®) devices and a network pruning technique to reduce network level energy consumption. First, we demonstrate low energy subquantum CBRAM devices exhibiting gradual switching characteristics important for implementing weight updates in hardware during unsupervised learning. Then we develop a network pruning algorithm that can be employed during training, different from previous network pruning approaches applied for inference only. Using a 512 kbit subquantum CBRAM array, we experimentally demonstrate high recognition accuracy on the MNIST dataset for digital implementation of unsupervised learning. Our hardware/software co-design approach can pave the way towards resistive memory based neuro-inspired systems that can autonomously learn and process information in power-limited settings
Space Radiation Effects in Conductive Bridging Random Access Memory
abstract: This work investigates the effects of ionizing radiation and displacement damage on the retention of state, DC programming, and neuromorphic pulsed programming of Ag-Ge30Se70 conductive bridging random access memory (CBRAM) devices. The results show that CBRAM devices are susceptible to both environments. An observable degradation in electrical response due to total ionizing dose (TID) is shown during neuromorphic pulsed programming at TID below 1 Mrad using Cobalt-60. DC cycling in a 14 MeV neutron environment showed a collapse of the high resistance state (HRS) and low resistance state (LRS) programming window after a fluence of 4.9x10^{12} n/cm^2, demonstrating the CBRAM can fail in a displacement damage environment. Heavy ion exposure during retention testing and DC cycling, showed that failures to programming occurred at approximately the same threshold, indicating that the failure mechanism for the two types of tests may be the same. The dose received due to ionizing electronic interactions and non-ionizing kinetic interactions, was calculated for each ion species at the fluence of failure. TID values appear to be the most correlated, indicating that TID effects may be the dominate failure mechanism in a combined environment, though it is currently unclear as to how the displacement damage also contributes to the response. An analysis of material effects due to TID has indicated that radiation damage can limit the migration of Ag+ ions. The reduction in ion current density can explain several of the effects observed in CBRAM while in the LRS.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
Neuromorphic Computing between Reality and Future Needs
Neuromorphic computing is a one of computer engineering methods that to model their elements as the human brain and nervous system. Many sciences as biology, mathematics, electronic engineering, computer science and physics have been integrated to construct artificial neural systems. In this chapter, the basics of Neuromorphic computing together with existing systems having the materials, devices, and circuits. The last part includes algorithms and applications in some fields
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Magnetic materials : fundamental synthesis of two-dimensional magnets and applications to neuromorphic computing
Two dimensional magnetic materials hold the promise of helping to achieve beyond CMOS computing tasks. 2D magnetic materials can be used in fabricating magnetic tunnel junctions with higher tunnel magnetoresistance which can then be applied to making new neuromorphic computing architectures primarily geared towards artificial intelligence and machine learning applications. In this work I summarize my synthesis and investigation of the properties of Crâ‚‚C which belongs to the group of 2D transition metal carbides or nitrides called MXenes. Crâ‚‚C has been predicted to have intrinsic half metallic ferromagnetic behaviors. These magnetic behaviors can be tuned based on the level of functionalization of the surface of the material. I show different parameters such as etchant, reaction temperature, and molar concentration that I have tuned in order to optimally derive Crâ‚‚C from its parent MAX phase Crâ‚‚AlC by removing the Al layer with a fluoride salt and hydrochloric acid. I also show how magnetic tunnel junctions (MTJs), which are two ferromagnetic layers with a tunnel barrier in between, can be used to make a synapse which is a neuromorphic computing primitive. The synapse circuit that I have proposed displays spike timing dependent plasticity which is an integral component of learning and memory in the brain. I show how different delay conditions between the presynaptic signal and the postsynaptic signal lead to currents of different magnitudes flowing through the ferromagnetic layer of the magnetic tunnel junction synapse. I also show how these currents move the domain wall both in micromagnetic simulation and using a domain wall MTJ Spice model that has been developed. I went on to wire four of these synapses together to observe the temporal dynamics of the system. My results show that the lower the delay between the presynaptic pulse and the postsynaptic pulse, the higher the current through the MTJ synapse and hence the larger the domain wall displacement. These studies pave the way for empirical understanding of the Crâ‚‚C MXene, including its potential magnetic properties, as well as doing online machine learning classification tasks with arrays of magnetic synapsesElectrical and Computer Engineerin