A number of recent efforts have attempted to design accelerators for popular machine learning algorithms, such as those involving convolutional and deep neural networks (CNNs and DNNs). These algorithms typically involve a large number of multiply-accumulate (dot-product) operations. A recent project, DaDianNao, adopts a near data processing approach, where a specialized neural functional unit performs all the digital arithmetic operations and receives input weights from adjacent eDRAM banks.This work explores an in-situ processing approach, where memristor crossbar arrays not only store input weights, but are also used to perform dot-product operations in an analog manner. While the use of crossbar memory as an analog dot-product engine is well known, no prior work has designed or characterized a full-fledged accelerator based on crossbars. In particular, our work makes the following contributions: (i) We design a pipelined architecture, with some crossbars dedicated for each neural network layer, and eDRAM buffers that aggregate data between pipeline stages. (ii) We define new data encoding techniques that are amenable to analog computations and that can reduce the high overheads of analog-to-digital conversion (ADC). (iii) We define the many supporting digital components required in an analog CNN accelerator and carry out a design space exploration to identify the best balance of memristor storage/compute, ADCs, and eDRAM storage on a chip. On a suite of CNN and DNN workloads, the proposed ISAAC architecture yields improvements of 14.8×, 5.5×, and 7.5× in throughput, energy, and computational density (respectively), relative to the state-of-the-art DaDianNao architecture.

ISAAC: a convolutional neural network accelerator with in-situ analog arithmetic in crossbars

International Technology Roadmap for Semiconductors 2003の要求清浄度について － シリコンウエハ表面と雰囲気環境に要求される清浄度, 分析方法の現状について －

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed

A Survey of Neuromorphic Computing and Neural Networks in Hardware.

Memristors are novel devices, useful as memory at all hierarchies. These devices can also behave as logic circuits. In this paper, the IMPLY logic gate, a memristor-based logic circuit, is described. In this memristive logic family, each memristor is used as an input, output, computational logic element, and latch in different stages of the computing process. The logical state is determined by the resistance of the memristor. This logic family can be integrated within a memristor-based crossbar, commonly used for memory. In this paper, a methodology for designing this logic family is proposed. The design methodology is based on a general design flow, suitable for all deterministic memristive logic families, and includes some additional design constraints to support the IMPLY logic family. An IMPLY 8-bit full adder based on this design methodology is presented as a case study.

Memristor-Based Material Implication (IMPLY) Logic: Design Principles and Methodologies

Memristor-based memory

This paper presents a motion compensated frame rate up-conversion algorithm that uses the expanded range of the motion trajectory to enhance the accuracy of motion estimation. In the proposed algorithm, an adaptive motion vector refinement is also proposed to correct false motion vectors. In addition, a recursive motion vector smoothing is proposed to smooth motion vectors considering true neighboring motion vectors only. Finally, we propose the weighted index-based bidirectional motion compensated interpolation to reduce artifacts. In the experiments using benchmark test sequences, the proposed algorithm improves the average PSNR of interpolated frames by up to 4.84 dB, when compared to the conventional algorithm.

Motion Compensated Frame Rate Up-Conversion Using Extended Bilateral Motion Estimation

The nonvolatile memory property of a memristor enables the realization of new methods for a variety of computational engines ranging from innovative memristive-based neuromorphic circuitry through to advanced memory applications. The nanometer-scale feature of the device creates a new opportunity for realization of innovative circuits that in some cases are not possible or have inefficient realization in the present and established design domain. The nature of the boundary, the complexity of the ionic transport and tunneling mechanism, and the nanoscale feature of the memristor introduces challenges in modeling, characterization, and simulation of future circuits and systems. Here, a deeper insight is gained in understanding the device operation, leading to the development of practical models that can be implemented in current computer-aided design (CAD) tools.

/pdf/memristive-device-fundamentals-and-modeling-applications-to-501zi9ckiu.pdf

Memristive Device Fundamentals and Modeling: Applications to Circuits and Systems Simulation

As conventional memory technologies are challenged by their technological physical limits, emerging technologies driven by novel materials are becoming an attractive option for future memory architectures. Among these technologies, Resistive Memories (ReRAM) created new possibilities because of their nanofeatures and unique I-V characteristics. One particular problem that limits the maximum array size is interference from neighboring cells due to sneak-path currents. A possible device level solution to address this issue is to implement a memory array using complementary resistive switches (CRS). Although the storage mechanism for a CRS is fundamentally different from what has been reported for memristors (low and high resistances), a CRS is simply formed by two series bipolar memristors with opposing polarities. In this paper, our intention is to introduce modeling principles that have been previously verified through measurements and extend the simulation principles based on memristors to CRS devices and, hence, provide an analytical approach to the design of a CRS array. The presented approach creates the necessary design methodology platform that will assist designers in implementation of CRS devices in future systems.

/pdf/an-analytical-approach-for-memristive-nanoarchitectures-4g6du77ebd.pdf

An Analytical Approach for Memristive Nanoarchitectures

Memristor-CMOS logic and digital computational components

This brief describes a new linear operational transconductance amplifier (OTA) and its application to a ninth-order Bessel filter. To improve the linearity of the OTA, we employ a mobility compensation circuit which combines the transistors operating in the triode and the subthreshold regions. The proposed technique enhances the linearity of the transconductance without loss of the input swing range. The proposed OTA shows /spl plusmn/0.5% Gm variation and the total harmonic distortion of less than - 60-dB over the input range of /spl plusmn/0.8-V. The ninth-order Bessel filter employing the proposed OTA has been implemented in a 0.35-/spl mu/m n-well CMOS process under 3.3-V supply voltage. It shows the cutoff frequency of 8 MHz and the power consumption of 65 mW.

Kyoung-Rok Cho

Papers

Motion Compensated Frame Rate Up-Conversion Using Extended Bilateral Motion Estimation

Memristive Device Fundamentals and Modeling: Applications to Circuits and Systems Simulation

An Analytical Approach for Memristive Nanoarchitectures

Memristor-CMOS logic and digital computational components

A novel CMOS operational transconductance amplifier based on a mobility compensation technique