Evolvable Hardware Design of Digital Circuits Based on Adaptive Genetic Algorithm

Shang, Qianyi; Chen, Lijun; Wang, Dandan; Tong, Ruoxiong; Peng, Peng

doi:10.1007/978-3-030-25128-4_97

Cited by 9 publications

(3 citation statements)

References 14 publications

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“…To mitigate this issue, several approaches can be employed to improve the evaluation process, such as partial evaluation [35], fitness approximation [11], etc. To enhance the performance of the GA and accelerate its convergence, the authors in [36] introduced an adaptive approach for adjusting the probability or value of the genetic operator parameters.…”

Section: Related Workmentioning

confidence: 99%

Self-Adaptive Learning Using Test Case Selection in Grammatical Evolution for Automatic Design of Digital Circuit

Gupt,

Kshirsagar,

Sarma

et al. 2024

Preprint

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In today’s digital age, the demand for increasingly complex digital circuits spans applications ranging from watches to spacecraft. To meet this demand, circuit design automation using Artificial Intelligence (AI) has emerged as a key solution due to manual processes’ time-consuming and error-prone nature. Among AI techniques, Evolutionary Algorithm (EA), notably Grammatical Evolution(GE), have shown promise. However, the computational cost of these algorithms remains a significant challenge, particularly concerning scalability and the high volume of test cases required for circuit design and synthesis. Balancing the trade-off between the necessity for extensive training data and the computational overhead of GE is crucial. The exponential increase in data volume with circuit complexity presents a challenging obstacle. While utilizing complete training data may ensure accurate circuit design, practical constraints often limit its feasibility. Furthermore, reducing data through selection risks compromising crucial information, while using complete data incurs prohibitively high training costs. This study proposes a novel self-adaptive learning approach within the framework of EAs to utilize training data adaptively and effectively. Our objective is to achieve comparable circuit solutions within a reduced runtime. We apply this technique to GE, naming it Self-adaptive Learning in GE (SLIG). SLIG divides the training data into subsets based on decreasing diversity, ensuring that the model is exposed to highly diverse scenarios early in the evolutionary process and gradually incorporating less diverse data. This method is rigorously evaluated across four digital circuits. The circuits are designed using Hardware Description Language (HDL), facilitating precise and efficient design, simulation, and synthesis. Experimental and statistical results indicate its effectiveness in producing comparable or superior results compared to standard GE, as measured in terms of circuits’ test scores, solution size, runtime efficiency, and performance parameters evaluated by the Cadence Genus Synthesis solution tool.

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Section: Related Workmentioning

confidence: 99%

Self-Adaptive Learning Using Test Case Selection in Grammatical Evolution for Automatic Design of Digital Circuit

Gupt,

Kshirsagar,

Sarma

et al. 2024

Preprint

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“…Genetic Algorithm (GA) is an algorithm that simulates the biological evolution process in nature and obtains the optimal value in the whole world. Firstly, a population was randomly initialized, and fitness function was used to evaluate the fitness of each individual [21][22][23]. Select the best with the selection function, and then cross generation, mutation of the children, repeat the cycle until the optimal solution is found.…”

Section: Mobile Information Systemsmentioning

confidence: 99%

Analysis of Network Public Opinion in New Media Based on BP Neural Network Algorithm

Yang¹

2022

Mobile Information Systems

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With the rapid development of Internet technology, new media is more and more favored by people and has become an important medium to control online public opinion. Social public opinion caused by new media is also more and more concerned by all walks of life. New media has the characteristics of fast information dissemination, wide dissemination range, and strong arbitrariness of news release. Positive online public voice or negative online public voice will have a very different impact on people’s lives. Some negative online public voice may even constitute a social crisis and seriously affect social public security. In order to analyze and predict the development trend of new media network public opinion, this paper presents a design of improved BP neural network model based on genetic algorithm, which is used to analyze public opinion in new media network. Experimental results show that this way has stronger processing ability and higher warning accuracy for online public opinion event index data. It can provide certain theoretical basis and data support for relevant departments to effectively prevent and manage new media network public opinion events.

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“…Chip-level dynamic calibration methods of analog and mixed-signal ICs are realized by utilizing configurable elements of the circuit serving as a calibration or tuning knobs and system performance evaluation setups [20][21][22][23][24]. As shown in Figure 1 this approach utilizes evolvable hardware (EHW), which refers to configurable electronic hardware that can be self-configured using ML and AI techniques such as metaheuristic optimization algorithms [25,26]. The evolutionary processing unit (EP) [27] runs the EHW to enable self-X properties for the system.…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Indirect Measurements for Power-Efficient In-Field Optimization of Configurable Analog Front-Ends with Self-X Properties: A Hardware Implementation

Zaman

Alraho

König

2023

Chips

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This paper presents a practical implementation and measurement results of power-efficient chip performance optimization, utilizing low-cost indirect measurement methods to support self-X properties (self-calibration, self-healing, self-optimization, etc.) for in-field optimization of analog front-end sensory electronics with XFAB 0.35 µm complementary metal oxide semiconductor (CMOS) technology. The reconfigurable, fully differential indirect current-feedback instrumentation amplifier (CFIA) performance is intrinsically optimized by employing a single test sinusoidal signal stimulus and measuring the total harmonic distortion (THD) at the output. To enhance the optimization process, the experience replay particle swarm optimization (ERPSO) algorithm is utilized as an artificial intelligence (AI) agent, implemented at the hardware level, to optimize the performance characteristics of the CFIA. The ERPSO algorithm extends the selection producer capabilities of the classical PSO methodology by incorporating an experience replay buffer to mitigate the likelihood of being trapped in local optima. Furthermore, the CFIA circuit has been integrated with a simple power-monitoring module to assess the power consumption of the optimization solution, to achieve a power-efficient and reliable configuration. The optimized chip performance showed an approximate 34% increase in power efficiency while achieving a targeted THD value of −72 dB, utilizing a 1 Vp-p differential input signal with a frequency of 1 MHz, and consuming approximately 53 mW of power. Preliminary tests conducted on the fabricated chip, using the default configuration pattern extrapolated from post-layout simulations, revealed an unacceptable performance behavior of the CFIA. Nevertheless, the proposed in-field optimization successfully restored the circuit’s performance, resulting in a robust design that meets the performance achieved in the design phase.

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Evolvable Hardware Design of Digital Circuits Based on Adaptive Genetic Algorithm

Cited by 9 publications

References 14 publications

Self-Adaptive Learning Using Test Case Selection in Grammatical Evolution for Automatic Design of Digital Circuit

Self-Adaptive Learning Using Test Case Selection in Grammatical Evolution for Automatic Design of Digital Circuit

Analysis of Network Public Opinion in New Media Based on BP Neural Network Algorithm

Low-Cost Indirect Measurements for Power-Efficient In-Field Optimization of Configurable Analog Front-Ends with Self-X Properties: A Hardware Implementation

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