Neuromorphic Computing From Materials to Systems Architecture

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Figure 7. I-V characteristic of a transistor, the basis of von Neumann architecture. This I-V is controlled by the voltage applied to the gate. Figure 8. I-V characteristic of a memristor, the basis of a possible neuromorphic architecture. This I-V is controlled by the history of the device. One possible implementation of a neuromorphic synapse is a hysteretic, two-‐‐terminal device: the memristor (see Figure 8). Typically, these types of devices exhibit a pinched (at zero voltage) I-‐‐V characteristic that is hysteretic. The type of hysteresis depends on the particular device implementation and material. An experimentally, easily accessible memristor material can be built from a strongly correlated oxide such as TiO2. The behavior of a memristor has been well established, although the detailed mechanism and how to modify it are still being investigated. This type of I-‐‐V characteristics lends itself to plasticity, namely, changing properties depending on the past history. It is expected that data transfer will be considerably reduced in the type of new neuromorphic architectures, with the consequent energetic savings. However, this cannot be completely eliminated as exemplified by the preponderance of connections through dendrites and axons in the brain. As a consequence, the interesting issue arises whether it would be possible to replace some of the electrically based data transfer to more energy efficient optical communications using reconfigurable optical elements and antennas. This may even be the basis of the development of a largely optically based data processor. Materials The development of neuromorphic devices also requires the use of strongly correlated, possibly complex, heterogeneous materials and heterostructures that are locally active so that they can be used to create an action potential. In many systems that are used as a basis for unconventional neuromorphic computing, the ultimate aim is to control the dynamical resistance (i.e., I-‐‐V characteristic) of a material or device. The control parameters can be categorized generally into two major classes: electronically driven and defect driven. Neuromorphic Computing: From Materials to Systems Architecture 19
Within each class, many materials systems and devices have been investigated in different contexts and depth. A (probably) non-‐‐exhaustive list is given in Tables 5 and 6 in the Appendix. Electronically driven materials rely on changes of a physical property due to variation in some control parameter such as temperature, current, voltage, magnetic field, or electromagnetic irradiation. Generally, they become useful if they exhibit negative differential resistance or capacitance and/or “threshold switching.” While for some systems the basic physics is well understood, for others, even the underlying phenomena are controversial. The types of materials that are actively being investigated and the fundamental issues that are being addressed are related to strong electronic correlations, phase transitions, charge, and spin propagation. In addition, efforts are underway to identify applications in the areas of spintronics, ferroelectric storage, and sensors. Electronically driven transitions include spin torque, ferroelectric, phase change, and metal-‐‐insulator transition based devices. The detailed physics of these systems differs significantly depending on the phenomena and the material system. In general, however, a scientific bottleneck exists because the detailed atomic-‐‐scale dynamics of these systems is still largely unknown. Defect driven materials take advantage of some of the fundamental properties, which are strongly affected by the presence of inhomogeneities. These include the formation of metallic filaments, inhomogeneous stress, and uniform and nonuniform oxygen diffusion. Strong debates arise concerning how well these phenomena can be controlled. Nevertheless, a number of experiments have proven that the inhomogeneities can be controlled by control parameters such as voltage, temperature, or electric fields. Typical systems of this type are oxygen ionic diffusion in oxides, formation of metallic filaments, and ionic front motion across metal-‐‐insulator-‐‐metal trilayers. Moreover, the endurance of these types of devices has been shown to exceed one trillion cycles—not as much as DRAM or SRAM—although research is expected to significantly increase this. Optically controlled materials and devices had a much more limited use in this context. This probably is because the eventual system that will emerge will likely be mostly electronic. On the other hand, the development of tunable optical elements may add an additional functionality to materials, which may be useful partially in this context. Fault tolerance is one of the principal requirements of the materials to be used. Defects and their effect play a crucial role in the behavior of these materials especially when incorporated into devices. This has not been the case with the materials that have been used until now in silicon-‐‐based technology, which strives for ever higher perfection Thus, the new, fault-‐‐tolerant materials may actually improve device performance. Neuromorphic Computing: From Materials to Systems Architecture 20
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Page 3 and 4: Contents EXECUTIVE SUMMARY .....
Page 5 and 6: 2. Creating a new computational
Page 7 and 8: typical power draw of 70 milli
Page 9 and 10: von Neumann Architecture Neuromo
Page 11 and 12: silicon devices may exhibit cer
Page 13 and 14: major impact for such an imple
Page 15 and 16: IMPLEMENTATION NEEDS In this se
Page 17 and 18: nature of the connectivity of
Page 19: 3. Threshold behavior, in which
Page 23 and 24: incorporated into commercial non
Page 25 and 26: networks already implemented in
Page 27 and 28: accomplished by unlocking the p
Page 29 and 30: Glossary Axon: Transmitting sign
Page 31 and 32: Neural networks digital hardware
Page 33 and 34: CATEGORY SYSTEM Oxides Phase Ch
Page 35 and 36: WORKSHOP LOGISTICS Roundtable Pa
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