Analog based Neuromorphic Systems on Low Power Current Mode Circuits

Abstract

Neuromorphic computing is the process used to appliance the neural system models. Formerly, it is referred to as the biological process and later it turned out to be the computing algorithms. Many neuromorphic algorithms represented as the neural figures such as neural spikes, fluctuated graphs, and synapses. The biological nervous system for instance consists of huge number of neurons and they collectively work to encode the stimulus of various senses. In case of neuromorphic computing, automated brain brings in the concept of efficient work carried out through artificial means. The neuromorphic computing thus evolves as a major technological advancement and the need of such technique is the need of the hour in various scientific as well as field applications. In existing techniques, the scaling, power and area are not efficient. This study attempts to address the major issues such as scaling and power. This paper explains the design on a non-spiking network which is used for population coding architecture. The model which is discussed in this paper is based on the analog domain and the current mode circuits are also involved. The input neuron model consists of current direction selector block, current scale block and minimum current block which all comprise to form the neuron model. This paper also brings out the possible outcome of low power constraints. This paper involves 180nm technology with which the power is measured. This paper brings out the simulations of both 180 and 90nm technologies. Apart from current scale block, minimum current block and current direction selector block, there are other blocks such as current splitter block and current mode low pass filter block, where all the circuits work under the sub-threshold condition. The power consumption obtained in the 180 nm technology is 58.838 μW and its energy equivalent is 1.765pJ. Neuromorphic computing is used as an application where the machines are being automated and such machines come with self-thinking capability. Neuromorphic computing design which is evolved from this paper is found to be more power ad energy efficient. The tool used is Cadence Virtuoso.