Digital hardware realization of a novel adaptive ink drop spread operator and its application in modeling and classification and on-chip training

Abstract

In artificial intelligence, proposing an efficient algorithm with an appropriate hardware implementation has always been a challenge because of the well-accepted fact that AI hardware implementations should ideally be comparable to biological systems in terms of hardware area. Active Learning Method (ALM) is a fuzzy learning algorithm inspired by human brain computations. Unlike traditional algorithms, which employ complicated computations, ALM tries to model human brain computations using qualitative and behavioral descriptions of the problem. The main computational engine in ALM is the Ink Drop Spread (IDS) operator, but this operator imposes high memory requirements and computational costs, making the ALM algorithm and its hardware implementation unsuitable for some of the applications. This paper proposes an adaptive alternative method for implementing the IDS operator; a method which results in a marked reduction in the algorithm's computational complexity and in the amount of memory required and hardware. To check its validity and performance, the method was used to carry out modeling and pattern classification tasks. This paper used challenging and real-world datasets and compared with wellknown algorithms (ANFIS and MLP) in software simulation and hardware implementation. Compared to traditional implementations of the ALM algorithm and other learning algorithms, the proposed FPGA implementation offers higher speed, less hardware, and improved performance, thus facilitating real-time application. Our ultimate goal in this paper was to present a hardware implementation with an on-chip training that allows it to adapt to its environment without dependency on the host system (on-chip learning).

Memristor Crossbar-Based Hardware Implementation of Type-2 Fuzzy Membership Function and On-Chip Learning

“…It can be concluded that the density of neurons in the brain is very high by considering the dimensions of the brain. So far, many computational structures were developed based on CMOS technology [12][13][14]. Since this technology faces challenges at the nanoscale, it is not a proper option for implementing Bio-Inspired systems and achieving high volumes of processing elements.…”