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Thermal Infrared Detectors: expanding performance limits using ultrafast electron microscopy

Talin, A.A.; Ellis, Scott R.; Bartelt, Norman C.; Leonard, Francois L.; Perez, Christopher P.; Celio, Km C.; Fuller, Elliot J.; Hughart, David R.; Garland, Diana; Marinella, Matthew J.; Michael, Joseph R.; Chandler, D.W.; Young, Steve M.; Smith, Sean M.; Kumar, Suhas K.

This project aimed to identify the performance-limiting mechanisms in mid- to far infrared (IR) sensors by probing photogenerated free carrier dynamics in model detector materials using scanning ultrafast electron microscopy (SUEM). SUEM is a recently developed method based on using ultrafast electron pulses in combination with optical excitations in a pump- probe configuration to examine charge dynamics with high spatial and temporal resolution and without the need for microfabrication. Five material systems were examined using SUEM in this project: polycrystalline lead zirconium titanate (a pyroelectric), polycrystalline vanadium dioxide (a bolometric material), GaAs (near IR), InAs (mid IR), and Si/SiO 2 system as a prototypical system for interface charge dynamics. The report provides detailed results for the Si/SiO 2 and the lead zirconium titanate systems.

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Achieving ideal accuracies in analog neuromorphic computing using periodic carry

Digest of Technical Papers - Symposium on VLSI Technology

Agarwal, Sapan A.; Jacobs-Gedrim, Robin B.; Hsia, Alexander W.; Hughart, David R.; Fuller, Elliot J.; Talin, A.A.; James, Conrad D.; Plimpton, Steven J.; Marinella, Matthew J.

Analog resistive memories promise to reduce the energy of neural networks by orders of magnitude. However, the write variability and write nonlinearity of current devices prevent neural networks from training to high accuracy. We present a novel periodic carry method that uses a positional number system to overcome this while maintaining the benefit of parallel analog matrix operations. We demonstrate how noisy, nonlinear TaOx devices that could only train to 80% accuracy on MNIST, can now reach 97% accuracy, only 1% away from an ideal numeric accuracy of 98%. On a file type dataset, the TaOx devices achieve ideal numeric accuracy. In addition, low noise, linear Li1-xCoO2 devices train to ideal numeric accuracies using periodic carry on both datasets.

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Designing an analog crossbar based neuromorphic accelerator

2017 5th Berkeley Symposium on Energy Efficient Electronic Systems, E3S 2017 - Proceedings

Agarwal, Sapan A.; Hsia, Alexander W.; Jacobs-Gedrim, Robin B.; Hughart, David R.; Plimpton, Steven J.; James, Conrad D.; Marinella, Matthew J.

Resistive memory crossbars can dramatically reduce the energy required to perform computations in neural algorithms by three orders of magnitude when compared to an optimized digital ASIC [1]. For data intensive applications, the computational energy is dominated by moving data between the processor, SRAM, and DRAM. Analog crossbars overcome this by allowing data to be processed directly at each memory element. Analog crossbars accelerate three key operations that are the bulk of the computation in a neural network as illustrated in Fig 1: vector matrix multiplies (VMM), matrix vector multiplies (MVM), and outer product rank 1 updates (OPU)[2]. For an NxN crossbar the energy for each operation scales as the number of memory elements O(N2) [2]. This is because the crossbar performs its entire computation in one step, charging all the capacitances only once. Thus the CV2 energy of the array scales as array size. This fundamentally better than trying to read or write a digital memory. Each row of any NxN digital memory must be accessed one at a time, resulting in N columns of length O(N) being charged N times, requiring O(N3) energy to read a digital memory. Thus an analog crossbar has a fundamental O(N) energy scaling advantage over a digital system. Furthermore, if the read operation is done at low voltage and is therefore noise limited, the read energy can even be independent of the crossbar size, O(1) [2].

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Resistive memory device requirements for a neural algorithm accelerator

Proceedings of the International Joint Conference on Neural Networks

Agarwal, Sapan A.; Plimpton, Steven J.; Hughart, David R.; Hsia, Alexander W.; Richter, Isaac; Cox, Jonathan A.; James, Conrad D.; Marinella, Matthew J.

Resistive memories enable dramatic energy reductions for neural algorithms. We propose a general purpose neural architecture that can accelerate many different algorithms and determine the device properties that will be needed to run backpropagation on the neural architecture. To maintain high accuracy, the read noise standard deviation should be less than 5% of the weight range. The write noise standard deviation should be less than 0.4% of the weight range and up to 300% of a characteristic update (for the datasets tested). Asymmetric nonlinearities in the change in conductance vs pulse cause weight decay and significantly reduce the accuracy, while moderate symmetric nonlinearities do not have an effect. In order to allow for parallel reads and writes the write current should be less than 100 nA as well.

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Power signatures of electric field and thermal switching regimes in memristive SET transitions

Journal of Physics. D, Applied Physics

Hughart, David R.; Gao, Xujiao G.; Mamaluy, Denis M.; Marinella, Matthew J.; Mickel, Patrick R.

We present a study of the 'snap-back' regime of resistive switching hysteresis in bipolar TaOx memristors, identifying power signatures in the electronic transport. Using a simple model based on the thermal and electric field acceleration of ionic mobilities, we provide evidence that the 'snap-back' transition represents a crossover from a coupled thermal and electric-field regime to a primarily thermal regime, and is dictated by the reconnection of a ruptured conducting filament. We discuss how these power signatures can be used to limit filament radius growth, which is important for operational properties such as power, speed, and retention.

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Development characterization and modeling of a TaOx ReRAM for a neuromorphic accelerator

Marinella, Matthew J.; Mickel, Patrick R.; Lohn, Andrew L.; Hughart, David R.; Bondi, Robert J.; Mamaluy, Denis M.; Hjalmarson, Harold P.; Stevens, James E.; Decker, Seth D.; Apodaca, Roger A.; Evans, Brian R.; Aimone, James B.; Rothganger, Fredrick R.; James, Conrad D.; DeBenedictis, Erik

This report discusses aspects of neuromorphic computing and how it is used to model microsystems.

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24 Results
24 Results