Approximate computing is a popular field for low power consumption that is used in several applications like image processing, video processing, multimedia and data mining. This Approximate computing is majorly performed with an arithmetic circuit particular with a multiplier. The multiplier is the most essential element used for approximate computing where the power consumption is majorly based on its performance. There are several researchers are worked on the approximate multiplier for power reduction for a few decades, but the design of low power approximate multiplier is not so easy. This seems a bigger challenge for digital industries to design an approximate multiplier with low power and minimum error rate with higher accuracy. To overcome these issues, the digital circuits are applied to the Deep Learning (DL) approaches for higher accuracy. In recent times, DL is the method that is used for higher learning and prediction accuracy in several fields. Therefore, the Long Short-Term Memory (LSTM) is a popular time series DL method is used in this work for approximate computing. To provide an optimal solution, the LSTM is combined with a meta-heuristics Jellyfish search optimisation technique to design an input aware deep learning-based approximate multiplier (DLAM). In this work, the jelly optimised LSTM model is used to enhance the error metrics performance of the Approximate multiplier. The optimal hyperparameters of the LSTM model are identified by jelly search optimisation. This fine-tuning is used to obtain an optimal solution to perform an LSTM with higher accuracy. The proposed pre-trained LSTM model is used to generate approximate design libraries for the different truncation levels as a function of area, delay, power and error metrics. The experimental results on an 8-bit multiplier with an image processing application shows that the proposed approximate computing multiplier achieved a superior area and power reduction with very good results on error rates.
In any kind of digital system, the processor and memories are used to play a vital role in today's trend. The processors and memories are done many critical tasks in the system. Whereas the processor used to do several functions and memories used to store and retrieve the data. But these processors and memories are more vulnerable to various hardware attacks. By using several new devices may lead to many security issues which the attackers can leverage to introduce a new hardware attack. Various hardware security (HS) studies have been presented to prevent hardware from a security issue. Some of the security issues that are occurred in hardware are overbuilding, piracy and reverse engineering (RE) and so on. In many works of literature, obfuscating and camouflaging are done in the netlist of hardware devices. Even though, these methods are highly overheads, and also not secured up to the level of expectation. Therefore, the main motive of Hardware security is to secure an Arithmetic Logic Unit (ALU) processor and memory unit from the various threats. In this work, a Configurable GDI based Locking cell (GLC) is proposed which is added as redundant to the original netlist of ALU and memory units for its hardware protection. The basic concept of configurable GLC is to perform a wire or inverter by using keys to obfuscate an attacker. This GLC logic can overcome the drawback of existing methods based on obfuscation and cryptographic techniques. The results show that proposed GLC is possible in any kind of memory system, all with low area and delay penalty.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.