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Sparse Data Acquisition on Emerging Memory Architectures

IEEE Access

Quach, Tu-Thach Q.; Agarwal, Sapan A.; James, Conrad D.; Marinella, Matthew J.; Aimone, James B.

Emerging memory devices, such as resistive crossbars, have the capacity to store large amounts of data in a single array. Acquiring the data stored in large-capacity crossbars in a sequential fashion can become a bottleneck. We present practical methods, based on sparse sampling, to quickly acquire sparse data stored on emerging memory devices that support the basic summation kernel, reducing the acquisition time from linear to sub-linear. The experimental results show that at least an order of magnitude improvement in acquisition time can be achieved when the data are sparse. In addition, we show that the energy cost associated with our approach is competitive to that of the sequential method.

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TYPE Journal Article YEAR 2019
ScopusOSTIDOI

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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TYPE Conference Poster YEAR 2017
ScopusOSTIDOI

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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TYPE Conference Poster YEAR 2017
ScopusOSTIDOI

A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications

Biologically Inspired Cognitive Architectures

James, Conrad D.; Aimone, James B.; Miner, Nadine E.; Vineyard, Craig M.; Rothganger, Fredrick R.; Carlson, Kristofor D.; Mulder, Samuel A.; Draelos, Timothy J.; Faust, Aleksandra; Marinella, Matthew J.; Naegle, John H.; Plimpton, Steven J.

Biological neural networks continue to inspire new developments in algorithms and microelectronic hardware to solve challenging data processing and classification problems. Here, we survey the history of neural-inspired and neuromorphic computing in order to examine the complex and intertwined trajectories of the mathematical theory and hardware developed in this field. Early research focused on adapting existing hardware to emulate the pattern recognition capabilities of living organisms. Contributions from psychologists, mathematicians, engineers, neuroscientists, and other professions were crucial to maturing the field from narrowly-tailored demonstrations to more generalizable systems capable of addressing difficult problem classes such as object detection and speech recognition. Algorithms that leverage fundamental principles found in neuroscience such as hierarchical structure, temporal integration, and robustness to error have been developed, and some of these approaches are achieving world-leading performance on particular data classification tasks. In addition, novel microelectronic hardware is being developed to perform logic and to serve as memory in neuromorphic computing systems with optimized system integration and improved energy efficiency. Key to such advancements was the incorporation of new discoveries in neuroscience research, the transition away from strict structural replication and towards the functional replication of neural systems, and the use of mathematical theory frameworks to guide algorithm and hardware developments.

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TYPE Journal Article YEAR 2017
ScopusOSTIDOI

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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TYPE Conference Poster YEAR 2016
ScopusOSTIDOI

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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TYPE Journal Article YEAR 2016
OSTIDOI

Energy scaling advantages of resistive memory crossbar based computation and its application to sparse coding

Frontiers in Neuroscience

Agarwal, Sapan A.; Quach, Tu-Thach Q.; Parekh, Ojas D.; Hsia, Alexander H.; DeBenedictis, Erik; James, Conrad D.; Marinella, Matthew J.; Aimone, James B.

The exponential increase in data over the last decade presents a significant challenge to analytics efforts that seek to process and interpret such data for various applications. Neural-inspired computing approaches are being developed in order to leverage the computational properties of the analog, low-power data processing observed in biological systems. Analog resistive memory crossbars can perform a parallel read or a vector-matrix multiplication as well as a parallel write or a rank-1 update with high computational efficiency. For an N × N crossbar, these two kernels can be O(N) more energy efficient than a conventional digital memory-based architecture. If the read operation is noise limited, the energy to read a column can be independent of the crossbar size (O(1)). These two kernels form the basis of many neuromorphic algorithms such as image, text, and speech recognition. For instance, these kernels can be applied to a neural sparse coding algorithm to give an O(N) reduction in energy for the entire algorithm when run with finite precision. Sparse coding is a rich problem with a host of applications including computer vision, object tracking, and more generally unsupervised learning.

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TYPE Journal Article YEAR 2016
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Results 26–50 of 63
Results 26–50 of 63