Field programmable gate array based sigmoid function implementation using differential lookup table and second order nonlinear function

Artificial neural network (ANN) is an established artificial intelligence technique that is widely used for solving numerous problems such as classification and clustering in various fields. However, the major problem with ANN is a factor of time. ANN takes a longer time to execute a huge number of neurons. In order to overcome this, ANN is implemented into hardware namely field-programmable-gate-array (FPGA). However, implementing the ANN into a field-programmable gate array (FPGA) has led to a new problem related to the sigmoid function implementation. Often used as the activation function for ANN, a sigmoid function cannot be directly implemented in FPGA. Owing to its accuracy, the lookup table (LUT) has always been used to implement the sigmoid function in FPGA. In this case, obtaining the high accuracy of LUT is expensive particularly in terms of its memory requirements in FPGA. Second-order nonlinear function (SONF) is an appealing replacement for LUT due to its small memory requirement. Although there is a trade-off between accuracy and memory size. Taking the advantage of the aforementioned approaches, this thesis proposed a combination of SONF and a modified LUT namely differential lookup table (dLUT). The deviation values between SONF and sigmoid function are used to create the dLUT. SONF is used as the first step to approximate the sigmoid function. Then it is followed by adding or deducting with the value that has been stored in the dLUT as a second step as demonstrated via simulation. This combination has successfully reduced the deviation value. The reduction value is significant as compared to previous implementations such as SONF, and LUT itself. Further simulation has been carried out to evaluate the accuracy of the ANN in detecting the object in an indoor environment by using the proposed method as a sigmoid function. The result has proven that the proposed method has produced the output almost as accurately as software implementation in detecting the target in indoor positioning problems. Therefore, the proposed method can be applied in any field that demands higher processing and high accuracy in sigmoid function outpu

Sigmoid Function Implementation Using the Unequal Segmentation of Differential Lookup Table and Second Order Nonlinear Function

This paper discusses the artificial neural network (ANN) implementation into a field programmable gate array (FPGA). One of the most difficult problem encounters is the complex equation of the activation function namely sigmoid function. The sigmoid function is used as learning function to train the neural network while its derivative is used as a network activation function for specifying the point at which the network should switch to a true state. In order to overcome this problem, two-steps approach which combined the unequal segmentation of the differential look-up table (USdLUT) and the second order nonlinear function (SONF) is proposed. Based on the analysis done, the deviation achieved using the proposed method is 95%. The result obtained is much better than the previous implementation that uses equal segmentation of differential look-up table

Review of hybrid analysis technique for malware detection

Malware is a problem spread out worldwide. Current techniques to analyze these malware are static analysis technique and dynamic analysis technique. Later, the two analysis technique is combined into a technique called hybrid analysis technique. This paper discusses on the current analysis technique and introduces a new approach towards the hybrid analysis technique by introducing memory analysis technique into it. The expected outcome of producing memory analysis technique in hybrid analysis technique will be discussed in the latter part of this paper

Ransomware: stages, detection and evasion

Ransomware attacks has been increasing lately with companies suffer monetarily, wasted business opportunity and wasted time. Big companies are now targeted as they are more profitable for ransomware threat actor. This paper discuses on stages of ransomware attacks starting from reconnaissance to extortion. It also discusses on steps that organization should take to prevent ransomware attack and several detection methods for ransomware. Other than that, it lists anti-analysis and evasion method used by ransomware to evade detections. Lastly, it discusses the latest ransomware attacks

Local Position Estimation Using an Artificial Neural Network Based Model with a Hardware Implementation

Efficient implementation of the activation function is an important part in the hardware design of artificial neural network. Sigmoid function is one of the most widely used activation function. In this paper, an efficient architecture for digital hardware implementation of sigmoid function is presented. The proposed method used second order nonlinear function (SONF) as a foundation and further improves the result by using 320 bits of read only memory (ROM) for storing a differential lookup table (differential LUT). The method proves to be more effective considering the smallest deviation of sigmoid function achieved in comparison to conventional LUT and SONF. Employing this method for hardware-based ANN in the indoor positioning system have shown that, ANN can detect the target position almost as accurate as software implementation with a speed 13 times faster. Thus the proposed idea is suitable to be implemented in a hardware-based ANN for various real-time applications

Sigmoid function implementation using the unequal segmentation of differential lookup table and second order nonlinear function

This paper discusses the artificial neural network (ANN) implementation into a field programmable gate array (FPGA). One of the most difficult problem encounters is the complex equation of the activation function namely sigmoid function. The sigmoid function is used as learning function to train the neural network while its derivative is used as a network activation function for specifying the point at which the network should switch to a true state. In order to overcome this problem, two-steps approach which combined the unequal segmentation of the differential look-up table (USdLUT) and the second order nonlinear function (SONF) is proposed. Based on the analysis done, the deviation achieved using the proposed method is 95%. The result obtained is much better than the previous implementation that uses equal segmentation of differential look-up table

Indoor Positioning Using Artificial Neural Network with Field Programmable Gate Array Implementation

Indoor positioning required fast and accurate result. This paper applied the artificial neural network (ANN) as a system for calculating the target in indoor environment. To speed up the calculation time, ANN then is run into field programmable gate array (FPGA). Since the original sigmoid function in ANN is not feasible to be applied into FPGA, two-steps sigmoid function calculation proposed by previous researcher then is used as a replacement. A new design of the FPGA is proposed to suite the requirement for implementing the previous researcher method. The results showing that FPGA can calculate 20 times faster with the maximum error 0.04 meters, slightly higher than the software implementation

Review of the Sigmoid Function Implementation into Digital Hardware

This paper discuss about the implementation of the sigmoid function into digital hardware. Several methods has been proposed such as lookup table (LUT), piecewise linear approximation (PWL), piecewise nonlinear approximation and CORDIC. Each method has their own advantages and disadvantages. Some researcher has start hybrid method by combining two or more the available methods for realizing the sigmoid function in digital hardware

Two-Step Implementation of Sigmoid Function for Artificial Neural Network in Field Programmable Gate Array

In this paper, a combination of second order nonlinear function (SONF) and differential look-up table (differential LUT) is introduced as a sigmoid function for implementing the artificial neural network (ANN) in field programmable gate array (FPGA). Implementing ANN on FPGA will overcome the slow response for real-time application and portable issues that arise in the software-based ANN. The output accuracy achieved by this two-step approach is ten times better than that of using only SONF and two times better than that of using conventional LUT. Thus the proposed idea is suitable to be implemented as a hardware-based ANN for various real-time applications

Door Access System via Fingerprint with GSM (ReSMART)

This work presents the prototype of door access system using biometric authentication (fingerprint) (ReSMART) for highly-protected area. The implementation of password authentication or ordinary key is somehow having the issues on security risk; therefore we propose ReSMART to enhance the process. ReSMART will verify the fingerprint of registered user, once the authentication is passed, the system will notify the authorized personal of door access activity through short message service (SMS)