Hardware Resource Utilization Research Articles

Reinforcement Learning-Based Resource Allocation Scheme of NR-V2X Sidelink for Joint Communication and Sensing

Joint communication and sensing (JCS) is becoming an important trend in 6G, owing to its efficient utilization of spectrums and hardware resources. Utilizing echoes of the same signal can achieve the object location sensing function, in addition to the V2X communication function. There is application potential for JCS systems in the fields of ADAS and unmanned autos. Currently, the NR-V2X sidelink has been standardized by 3GPP to support low-latency high-reliability direct communication. In order to combine the benefits of both direct communication and JCS, it is promising to extend existing NR-V2X sidelink communication toward sidelink JCS. However, conflicting performance requirements arise between radar sensing accuracy and communication reliability with the limited sidelink spectrum. In order to overcome the challenges in the distributed resource allocation of sidelink JCS with a full-duplex, this paper has proposed a novel consecutive-collision mitigation semi-persistent scheduling (CCM-SPS) scheme, including the collision detection and Q-learning training stages to suppress collision probabilities. Theoretical performance analyses on Cramér–Rao Lower Bounds (CRLBs) have been made for the sensing of sidelink JCS. Key performance metrics such as CRLB, PRR and UD have been evaluated. Simulation results show the superior performance of CCM-SPS compared to similar solutions, with promising application prospects.

Optimization and Construction of a Deep Learning Model for Breast Cancer Segmentation

The research focuses on optimizing and developing a breast cancer segmentation model utilizing multi-GPU arrays to maximize hardware resource utilization. By exploring various architectural strategies, the study aims to enhance resource allocation efficiency despite existing limitations. Detailed evaluations using mammography datasets have demonstrated significant improvements in tumor detection capabilities. This technology holds the potential to revolutionize breast cancer detection, a critical advancement given the global impact of the disease. Training data analysis confirms the scalability of these results across diverse hardware configurations, ensuring high efficiency and reliability. The study employs modern architectures, contributing valuable insights to the field of breast cancer segmentation and advancing medical imaging technologies. The development of efficient and clinically viable solutions is imperative in contemporary medical image analysis. While state-of-the-art deep learning architectures offer impressive capabilities, their substantial computational demands pose barriers to widespread clinical adoption. This research addresses the need for solutions that efficiently process large datasets while maintaining diagnostic accuracy, facilitating integration into clinical workflows and reducing operational costs.

A Hardware-Efficient Novelty-Aware Spike Sorting Approach for Brain-Implantable Microsystems.

Unsupervised spike sorting, a vital processing step in real-time brain-implantable microsystems, is faced with the prominent challenge of managing nonstationarity in neural signals. In long-term recordings, spike waveforms gradually change and new source neurons are likely to become activated. Adaptive spike sorters combined with on-implant training units effectively process the nonstationary signals at the cost of high hardware resource utilization. On the other hand, static approaches, while being hardware-friendly, are subjected to decreased processing performance in such recordings where the neural signal characteristics gradually change. To strike a balance between the hardware cost and processing performance, this study proposes a hardware-efficient novelty-aware spike sorting approach that is capable of dealing with both variated spike waveforms and spike waveforms generated from new source neurons. Its improved hardware efficiency compared to adaptive ones and capability of dealing with nonstationary signals make it attractive for implantable applications. The proposed novelty-aware spike sorting especially would be a good fit for brain-computer interfaces where long-term, real-time interaction with the brain is required, and the available on-implant hardware resources are limited. Our unsupervised spike sorting benefits from a novelty detection process to deal with neural signal variations. It tracks the spike features so that in case of detecting an unexpected change (novelty detection) both on and off-implant parameters are updated to preserve the performance in new state. To make the proposed approach agile enough to be suitable for brain implants, the on-implant computations are reduced while the computational burden is realized off-implant. The performance of our proposed approach is evaluated using both synthetic and real datasets. The results demonstrate that, in the mean, it is capable of detecting 94.31% of novel spikes (wave-drifted or emerged spikes) with a classification accuracy (CA) of 96.31%. Moreover, an FPGA prototype of the on-implant circuit is implemented and tested. It is shown that in comparison to the OSORT algorithm, a pivotal spike sorting method, our spike sorting provides a higher CA at significantly lower hardware resources. The proposed circuit is also implemented in a 180-nm standard CMOS process, achieving a power consumption of 1.78[Formula: see text][Formula: see text] per channel and a chip area of 0.07[Formula: see text]mm2 per channel.

Automated search of an optimal configuration of FETI-based algorithms with the swarm and evolutionary algorithms

Finite Element Tearing and Interconnecting (FETI) methods are used in the engineering community to solve extremely large engineering simulations on clusters and supercomputers with thousands of computational nodes. This paper focuses on minimizing the execution time of these methods by searching for an optimal configuration with swarm and evolutionary algorithms (SEAs). It incorporates optimization of an expensive cost function (taking tens to hundreds of seconds) of discrete search space with up to hundreds of thousands of combinations constrained by its boundaries and incompatible individuals (invalid combination of FETI solver parameters). In addition, the optimization occurs in real time, i.e., during a simulation. Hence, the number of objective function evaluations must remain low (tens to lower hundreds). The paper compares the performance of 3 basic SEAs with a reduced population size (Differential Evolution, Particle Swarm Optimization, and Self-Organizing Migrating Algorithm), 4 micro SEAs (Micro Differential Evolution Ray, Improved Micro-Particle Swarm Optimization, Micro-Particle Swarm Optimization, and Micro-Genetic Algorithm), and a random search on 4 distinct time-dependent simulations of a heat transfer. The experiments show that basic SEAs with a small population (about 5 individuals) and a penalty system as protection against incompatible configurations represent the most effective solution. One can use them to find the optimal configuration of FETI-based methods in approximately 130 evaluations. It can improve the utilization of expensive hardware resources of modern computational clusters.

FPGA implementation of an optimized neural network for CFD acceleration

In this work, an evaluation of FPGAs as the central computation platform in domain-specific AI-accelerated CFD simulations is performed. This evaluation is performed in three categories: power efficiency, speed, and accuracy. The specific domain in the study is the FDA nozzle benchmark, which is simulated using SimpleFoam, a laminar solver that is a component of the OpenFOAM CFD toolbox. The proposed AI model is a low-parameter feed-forward neural network with three fully connected layers, trained using steady-state solutions distinguished by various Reynolds numbers, all of which are computed by the OpenFOAM framework. The proposed model can then generate the steady-state CFD simulation result given the initial few iterations generated by the solver. Moreover, this paper introduces a hardware implementation for inference of the simulation results using an SoC chip with minimal hardware resource utilization. The suggested hardware design is developed from scratch for Zynq-7000 System-on-Chip, using only VHDL, and requiring no dependencies on third-party commercial AI frameworks or costly FPGA boards designed for AI-related applications. The proposed workflow in the test case study achieves a 98% reduction in simulation time while maintaining relatively high accuracy and a 95.6% reduction in energy consumption compared with the regular CFD workflow.

Design and Implementation of a 7-Person Voting Device based on Quartus

As the complexity of digital system design continues to increase, efficient and precise decision support systems have become increasingly important. This paper presents the design and implementation of a 7-voter decision system based on Altera’s Quartus software. The voter is applied in the core decision-making process to ensure the accuracy and efficiency of decisions. The study begins by outlining the design principles of the 7-voter decision system, using a design philosophy that minimizes timing delays and optimizes hardware resource utilization. A complex majority voting logic circuit is implemented using high-level hardware description languages (VHDL/Verilog), ensuring the advanced nature and applicability of the voting circuit. In terms of the logical implementation of the voting circuit, the paper conducts an in-depth analysis of the optimization of combinational and sequential logic and standardizes the input and output to enhance the signal stability and the system’s resistance to interference. On the Quartus platform, the design process of the voting system is elaborated in detail, with careful consideration given to boundary conditions and hardware compatibility. The implementation of the voter has undergone rigorous simulation and testing. Strict parameter setting for simulations and waveform analysis confirm the correctness of the circuit, and fault simulation tests further confirm its reliability. Performance indicators such as response time and percentage of resource utilization have been quantitatively analyzed and meet the design objectives set beforehand. Overall, this research not only realizes a highperformance 7-voter decision system but also provides an effective case study for applications on the high-performance Quartus design platform. Future research can build on the current work to further optimize algorithms and hardware, explore the potential for the application of the voter in a broader range of fields, and adapt to the new features of the Quartus platform to achieve more powerful decision support functions.

A resource-aware workload scheduling method for unbalanced GEMMs on GPUs

Abstract GEMM (General Matrix Multiplication) serves as a fundamental operator for deep learning computations. Especially in attention-based deep learning models, such as Bert, GPT, and SAM, the sizes of matrices involved in GEMMs exhibit an unbalanced distribution due to the variable input, resulting in the low utilization of hardware resources. To address the issue, this paper proposes inserting a novel GEMM processing layer into the deep learning inference stack and using an adaptive load balancing method to partition and schedule GEMM computation tasks. The method is implemented with hardware runtime resource information, such as the occupancy of computing units, etc. Experiment results show the remarkable performance of our method in unbalanced input GEMM scenarios, achieving an average performance improvement of 2.3x. The method also performs well in attention-based models (GPT-2 and SAM), achieving an average inference speed improvement of 1.1x. These findings highlight the effectiveness of resource-aware algorithm optimization, especially for computation task scheduling.

Lightweight skin cancer detection IP hardware implementation using cycle expansion and optimal computation arrays methods

Skin cancer is recognized as one of the most perilous diseases globally. In the field of medical image classification, precise identification of early-stage skin lesions is imperative for accurate diagnosis. However, deploying these algorithms on low-cost devices and attaining high-efficiency operation with minimal energy consumption poses a formidable challenge due to their intricate computational demands. This study proposes a lightweight hardware design based on a convolutional neural network (CNN) for real-time processing of skin disease classifiers on portable devices. Our skin cancer recognition processor utilizes an optimally parallel designed processing engine (PE) for global computation, which greatly reduces hardware resource utilization by multiplexing of computational unit circuits. In addition, a design approach that provides loop unrolling effectively reduces the number of data accesses, thereby reducing computational complexity and logic resource requirements. The hardware circuits in this design perform data inference in convolutional, pooling, and fully connected layers based on 16-bit floating-point numbers. Evaluation of the HAM10000 database dataset shows that the architecture achieves an average classification accuracy of 97.8 %. We are the first to implement an all-hardware FPGA-based skin cancer detection platform that offers a 3.5x speedup in recognition compared to existing skin cancer accelerators at 50 MHz while consuming only 0.48 W of power. The implementation of this hardware architecture meets the major constraints of portable devices, featuring low resource utilization, low power consumption, and cost-effectiveness, while still providing efficient classification and high accuracy results.

A Unified Hardware Design for Multiplication, Division, and Square Roots Using Binary Logarithms

Multiplication, division, and square root operations introduce significant challenges in digital signal processing (DSP) systems, traditionally requiring multiple operations that increase execution time and hardware complexity. This study presents a novel approach that leverages binary logarithms to perform these operations using only addition, subtraction, and shifts, enabling a unified hardware implementation—a marked departure from conventional methods that handle these operations separately. The proposed design, involving logarithm and antilogarithm calculations, exhibits an algebraically symmetrical pattern that further optimizes the processing flow. Additionally, this study introduces innovative log-domain correction terms specifically designed to minimize computation errors—a critical improvement over existing methods that often struggle with precision. Compared to standard hardware implementations, the proposed design significantly reduces hardware resource utilization and power consumption while maintaining high operational frequency.

Quantized CNN-based efficient hardware architecture for real-time hand gesture recognition

Nowadays, Convolutional Neural Networks (CNN) have been widely adopted for vision-based hand gesture recognition. Several existing CNN architectures designed for gesture classification perform well with high accuracy but require a high memory footprint and processing when deployed on low-power embedded devices. To address this issue, we present a quantized CNN-based efficient framework for meeting real-life hand gesture recognition challenges. The proposed quantized CNN is designed and implemented using the FINN-based pipelined streaming architecture on an FPGA. Moreover, hardware-based optimizations are used to minimize the resources needed and to achieve fast memory access. Experimental results demonstrate that the developed recognition system achieves an average accuracy of 92% on the numeral database of Indian Sign Language (ISL). Additionally, our optimized design attains an inference latency of 0.85ms for real-time single gesture prediction on the PYNQ Zynq Ultrascale (ZU) FPGA, consuming only 3.63 W of power. The proposed design achieves a better trade-off between hardware resource utilization and speed performance, over previous designs.

Runtime support for CPU-GPU high-performance computing on distributed memory platforms

IntroductionHardware heterogeneity is here to stay for high-performance computing. Large-scale systems are currently equipped with multiple GPU accelerators per compute node and are expected to incorporate more specialized hardware. This shift in the computing ecosystem offers many opportunities for performance improvement; however, it also increases the complexity of programming for such architectures.MethodsThis work introduces a runtime framework that enables effortless programming for heterogeneous systems while efficiently utilizing hardware resources. The framework is integrated within a distributed and scalable runtime system to facilitate performance portability across heterogeneous nodes. Along with the design, this paper describes the implementation and optimizations performed, achieving up to 300% improvement on a single device and linear scalability on a node equipped with four GPUs.ResultsThe framework in a distributed memory environment offers portable abstractions that enable efficient inter-node communication among devices with varying capabilities. It delivers superior performance compared to MPI+CUDA by up to 20% for large messages while keeping the overheads for small messages within 10%. Furthermore, the results of our performance evaluation in a distributed Jacobi proxy application demonstrate that our software imposes minimal overhead and achieves a performance improvement of up to 40%.DiscussionThis is accomplished by the optimizations at the library level and by creating opportunities to leverage application-specific optimizations like over-decomposition.

Projective quantum eigensolver via adiabatically decoupled subsystem evolution: A resource efficient approach to molecular energetics in noisy quantum computers.

Quantum computers hold immense potential in the field of chemistry, ushering new frontiers to solve complex many-body problems that are beyond the reach of classical computers. However, noise in the current quantum hardware limits their applicability to large chemical systems. This work encompasses the development of a projective formalism that aims to compute ground-state energies of molecular systems accurately using noisy intermediate scale quantum (NISQ) hardware in a resource-efficient manner. Our approach is reliant upon the formulation of a bipartitely decoupled parameterized ansatz within the disentangled unitary coupled cluster framework based on the principles of nonlinear dynamics and synergetics. Such decoupling emulates total parameter optimization in a lower dimensional manifold, while a mutual synergistic relationship among the parameters is exploited to ensure characteristic accuracy via a non-iterative energy correction. Without any pre-circuit measurements, our method leads to a highly compact fixed-depth ansatz with shallower circuits and fewer expectation value evaluations. Through analytical and numerical demonstrations, we establish the method's superior performance under noise while concurrently ensuring requisite accuracy in future fault-tolerant systems. This approach enables rapid exploration of emerging chemical spaces by the efficient utilization of near-term quantum hardware resources.

Arterial Distension Monitoring Scheme Using FPGA-Based Inference Machine in Ultrasound Scanner Circuit System.

This paper presents an arterial distension monitoring scheme using a field-programmable gate array (FPGA)-based inference machine in an ultrasound scanner circuit system. An arterial distension monitoring requires a precise positioning of an ultrasound probe on an artery as a prerequisite. The proposed arterial distension monitoring scheme is based on a finite state machine that incorporates sequential support vector machines (SVMs) to assist in both coarse and fine adjustments of probe position. The SVMs sequentially perform recognitions of ultrasonic A-mode echo pattern for a human carotid artery. By employing sequential SVMs in combination with convolution and average pooling, the number of features for the inference machine is significantly reduced, resulting in less utilization of hardware resources in FPGA. The proposed arterial distension monitoring scheme was implemented in an FPGA (Artix7) with a resource utilization percentage less than 9.3%. To demonstrate the proposed scheme, we implemented a customized ultrasound scanner consisting of a single-element transducer, an FPGA, and analog interface circuits with discrete chips. In measurements, we set virtual coordinates on a human neck for 9 human subjects. The achieved accuracy of probe positioning inference is 88%, and the Pearson coefficient (r) of arterial distension estimation is 0.838.

GateKeeper: An UltraLite malicious traffic identification method with dual-aspect optimization strategies on IoT gateways

The Internet of Things (IoT) landscape is booming, and a wide range of IoT endpoints has been integrated into various aspects of life, opening opportunities for malicious attacks that compromise IoT security. Consequently, effective malicious traffic identification methods play a crucial role in ensuring IoT security. However, the current lightweight methods face two main challenges. Firstly, they often generate feature sets that are time-consuming to construct and less adaptable to various scenarios. Secondly, classification models built upon inefficient neural networks incur a significant computational overhead. To address these drawbacks, in this work, we propose GateKeeper, which is an UltraLite method for malicious traffic identification on the IoT gateway. We first propose a base model as the cornerstone and target for subsequent optimization. Then we propose dual-aspect optimization strategies for reducing the input dimension and simplifying the model structure, i.e., Key Bytes Selection (KBS) and Attention Module Simplification (AMS) strategy. Finally, we obtain the optimized UltraLite model-GateKeeper, specifically tailored for integration into IoT gateway deployments, where high-speed real-time identification is paramount. Our extensive experiments demonstrate that GateKeeper performs remarkably in identifying malicious IoT traffic, achieving over 97% accuracy in all three IoT malicious traffic classification benchmark tasks, outperforming six state-of-the-art methods. Besides, GateKeeper’s parameters and FLOPs are 65% smaller than those of existing methods. Moreover, in the case of an IoT gateway platform, GateKeeper demonstrates remarkably low time overhead and hardware resource utilization, surpassing state-of-the-art methods.

Establishing operator trust in machine learning for enhanced reliability and safety in nuclear Power Plants

The advancement of safety and reliability in Nuclear Power Plants (NPP) is essential for ensuring the protection of human life, the environment, and the sustainable use of clean energy. The key factor to achieve this goal is enhancing operator performance, as their decisions play a crucial role during reactor accidents. In this sense, the integration of artificial intelligence (AI) with safety systems in NPP could provide suggestions and recommendations to the operator, leading to a quick and accurate response. However, the complexity of models like Convolutional Neural Networks (CNN), often considered as black boxes, presents challenges for operators to comprehend the decision-making process. This difficulty leads to a lack of trust in the model's output. Therefore, ensuring algorithm transparency becomes crucial to maintain a positive relationship between humans and AI technology. In this study, the necessary data were collected through the automation technique implemented in the (PCTRAN) software. Subsequently, CNN was employed to classify various design-based accidents (DBA), accompanied by transfer learning to optimize accuracy. Finally, Shapley Additive Explanation (SHAP) was used to provide outcome explainability. The results show the effectiveness of automation in reducing time consumption and optimizing hardware resource utilization. Among the pre-trained models, MobileNet exhibits superior performance by requiring a smaller dataset and less time, achieving the highest accuracy, and demonstrating an optimal balance of (correct and incorrect) data prediction within the classes. Furthermore, the use of Explainable AI (XAI) provides result transparency and clarifies the reasons behind CNN incorrect predictions.

Adaptive neuro fuzzy technology to enhance PID performances within VCA for grid-connected wind system under nonlinear behaviors: FPGA hardware implementation

Wind Energy Conversion Systems (WECSs) exhibit nonlinear behavior due to internal and external disturbances, which can significantly impact the control performance. Firstly, this paper proposes a Vector Control Approach (VCA) based on Adaptive Neuro Fuzzy (ANF) of Proportional Integral Derivative (PID) controllers for a variable speed Permanent Magnet Synchronous Generator (PMSG)-based WECS connected to the grid to address these issues. The proposed ANF-PID controller combines the robustness of the fuzzy logic with the neural network's performance to ensure high accuracy, excellent tracking, and robustness against the WECS's nonlinear behavior. The neural network automates the placement of fuzzy logic membership functions. Secondly, this paper suggests a real-time hardware implementation of the VCA based on ANF-PID using a Field Programmable Gate Array (FPGA) to reduce the processing time and the WECS's sampling period. The effectiveness of the VCA based on ANF-PID controllers is validated by conducting a numerical simulation within the Matlab-Simulink using the Xilinx System Generator tool, alongside an experimental in-the-loop hardware implementation using FPGA board. Different scenarios are conducted to validate the robustness and stability of the proposed VCA based on ANF-PID control schemes, which is compared to Conventional Fuzzy PID (C-FPID) and PID controllers. The results exhibit that the VCA based on ANF-PID controllers outperforms VCA-C-FPID and VCA-PID controllers in terms of precision, tracking accuracy, and robustness against the WECS's nonlinear behavior and the PMSG parameter variations. The performances of the proposed controllers are tested under some criteria. In fact, the proposed ANF-PID controller provides a Mean Absolute Error (MAE), a Mean Squared Error (MSE) and a Root Mean Squared Error (RMSE) of 8.1911 × 10−04, 8.4181 × 10−05 and 0.0092, respectively. Compared to the C-FPID controller, the proposed controller offers an improvement gain of 98.96 %, 99.61 %, and 93.79 % of MAE, MSE, and RMSE, respectively. When compared to the PID controller, its improvement is even more significant, with a reduction of 99.9919 %, 99.9999 %, and 99.9288 % of MAE, MSE, and RMSE, respectively. In addition, the ANF-PID-based VCA utilizes hardware resources on the FPGA efficiently, requiring only 65.60 % slices LUT, 0.28 % LUTRAM, and 22.99 % FF. This makes it a more cost-effective option compared to the VCA-C-FPID controller for diverse control systems.

FPGA-base object tracking: integrating deep learning and sensor fusion with Kalman filter

This research presents an integrated approach for object detection and tracking in autonomous perception systems, combining deep learning techniques for object detection with sensor fusion and field programmable gate array (FPGA-based) hardware implementation of the Kalman filter. This approach is suitable for applications like autonomous vehicles, robotics, and augmented reality. The study explores the seamless integration of pre-trained deep learning models, sensor data from a depth camera, real-sense D435, and FPGA-based Kalman filtering to achieve robust and accurate 3D position and 2D size estimation of tracked objects while maintaining low latency. The object detection and feature extraction are implemented on a central processing unit (CPU), and the Kalman filter sensor fusion with universal asynchronous receiver transmitter (UART) communication is implemented on a Basys 3 FPGA board that performs 8 times faster compared to the software approach. The experimental result provides the hardware resource utilization of about 29% of look-up tables, 6% of lookup table RAMs (LUTRAM), 15% of Flip-flops, 32% of Block-RAM, 38% of DSP blocks operating at 100 MHz, and 230400 baud rates for the UART. The whole FPGA design executes at 2.1 milliseconds, the Kalman filter executes at 240 microseconds, and the UART at 1.86 milliseconds.

FPGA Implementation of a Pipelined Kalman Filter for Object Tracking in Two Dimensions

In a location tracking system for a moving object, not only accuracy but also real-time processing are important factors. The Kalman filter, as a recursive function, stands out as one of the prominent algorithms for object tracking. It continuously compares measured data with predicted values based on the system characteristics in real time, and then corrects the error of the predicted values while considering the noise of both system and measured data. This paper focuses on designing a hardware-based Kalman filter for object tracking in two dimensions. Following an analysis of the Kalman filter algorithm, the blocks capable of parallel processing are identified and configured to be processed in parallel, effectively reducing data processing time. The clock speed is enhanced by using the pipeline technique. In addition, the time-sharing technique is applied to increase the utilization of hardware resources and reduce the area. Data was processed at 32-bit floating points to uphold accuracy comparable to software-implemented Kalman filters. The proposed Kalman filter architecture is designed using verilog HDL and then simulated in Synopsys VCS/Verdi. And the accuracy is verified by comparing results with a software-based Kalman filter designed using MATLAB. It was implemented using Zynq ZYNQ-7 ZC702 Programmable logic via Xilnix Vivado, and can operate at 33MHz. It takes a total of 44 clocks, or 1.32 us, to process one data. Therefore, it was confirmed that the designed Kalman filter hardware is suitable for real-time processing.

Remote field-programmable gate array laboratory for signal acquisition and design verification

A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embedded-system design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.

Implementation of FIR Filters through Inner product Units and Parallel Accumulations

Finite Impulse Response (FIR) filters are pivotal in digital signal processing, finding applications in diverse fields like audio processing, telecommunications, and biomedical signal analysis. This work presents an enhanced implementation methodology for FIR filters utilizing inner product computation and parallel accumulations. In the existing, FIR filters are typically implemented using convolution techniques, basic adders, and multipliers, which involve sequential processing and intensive computational resources. This method often leads to latency issues and limits real-time applications. Moreover, traditional implementations suffer from inefficiencies in utilizing hardware resources optimally, leading to suboptimal performance. The proposed methodology overcomes these limitations by leveraging inner product computations and parallel accumulation techniques. By exploiting inherent parallelism in the filtering process, the proposed method significantly reduces latency and enhances throughput.