Design and Analysis of Low-Power and High Speed Approximate Adders Using CNFETs

Abstract

Adders are constituted as the fundamental blocks of arithmetic circuits and are considered important for computation devices. Approximate computing has become a popular and developing area, promising to provide energy-efficient circuits with low power and high performance. In this paper, 10T approximate adder (AA) and 13T approximate adder (AA) designs using carbon nanotube field-effect transistor (CNFET) technology are presented. The simulation for the proposed 10T approximate adder and 13T approximate adder designs were carried out using the HSPICE tool with 32 nm CNFET technology. The metrics, such as average power, power-delay product (PDP), energy delay product (EDP) and propagation delay, were carried out through the HSPICE tool and compared to the existing circuit designs. The supply voltage Vdd provided for the proposed circuit designs was 0.9 V. The results indicated that among the existing full adders and approximate adders found in the review of adders, the proposed circuits consumed less PDP and minimum power with more accuracy.

Hetero‐Integrated InGaAs Photodiode and Oxide Memristor‐Based Artificial Optical Nerve for In‐Sensor NIR Image Processing

“…[5] In particular, conventional computing units consist of multiple electronic components to effectively process the acquired signals. The complementary-metal-oxide semiconductor (CMOS) technology-based architectures require more than five transistors even for simple arithmetic computation, [6] which significantly increases the complexity of backplane circuitry for in-sensor computing. [5] Unlike the CMOS architectures that process the signals in the digital domain, emerging neuromorphic computing allows in-memory processing in the analog domain, reducing the energy consumption, data processing time, and footprint of the computing devices.…”