Neuro-memristive Circuits for Edge Computing: A review

The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing

Root finding with threshold circuits

We show that for any constant d, complex roots of degree d univariate rational (or Gaussian rational) polynomials---given by a list of coefficients in binary---can be computed to a given accuracy by a uniform TC^0 algorithm (a uniform family of constant-depth polynomial-size threshold circuits). The basic idea is to compute the inverse function of the polynomial by a power series. We also discuss an application to the theory VTC^0 of bounded arithmetic.Comment: 19 pages, 1 figur

Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition

A neuromorphic chip that combines CMOS analog spiking neurons and memristive synapses offers a promising solution to brain-inspired computing, as it can provide massive neural network parallelism and density. Previous hybrid analog CMOS-memristor approaches required extensive CMOS circuitry for training, and thus eliminated most of the density advantages gained by the adoption of memristor synapses. Further, they used different waveforms for pre and post-synaptic spikes that added undesirable circuit overhead. Here we describe a hardware architecture that can feature a large number of memristor synapses to learn real-world patterns. We present a versatile CMOS neuron that combines integrate-and-fire behavior, drives passive memristors and implements competitive learning in a compact circuit module, and enables in-situ plasticity in the memristor synapses. We demonstrate handwritten-digits recognition using the proposed architecture using transistor-level circuit simulations. As the described neuromorphic architecture is homogeneous, it realizes a fundamental building block for large-scale energy-efficient brain-inspired silicon chips that could lead to next-generation cognitive computing.Comment: This is a preprint of an article accepted for publication in IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol 5, no. 2, June 201

Modified Level Restorers Using Current Sink and Current Source Inverter Structures for BBL-PT Full Adder

Full adder is an essential component for the design and development of all types of processors like digital signal processors (DSP), microprocessors etc. In most of these systems adder lies in the critical path that affects the overall speed of the system. So enhancing the performance of the 1-bit full adder cell is a significant goal. In this paper, we proposed two modified level restorers using current sink and current source inverter structures for branch-based logic and pass-transistor (BBL-PT) full adder [1]. In BBL-PT full adder, there lies a drawback i.e. voltage step existence that could be eliminated in the proposed logics by using the current sink inverter and current source inverter structures. The proposed full adders are compared with the two standard and well-known logic styles, i.e. conventional static CMOS logic and Complementary Pass transistor Logic (CPL), demonstrated the good delay performance. The implementation of 8-bit ripple carry adder based on proposed full adders are finally demonstrated. The CPL 8-bit RCA and as well as the proposed ones is having better delay performance than the static CMOS and BBL-PT 8-bit RCA. The performance of the proposed BBL-PT cell with current sink & current source inverter structures are examined using PSPICE and the model parameters of a 0.13 µm CMOS process