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La influe= ncia del aprendizaje automático en el futuro del análisis de tiempo estático<= /span>

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The influence of machine learning on the future of static time analysis Julio Julio Torres Tello.=

 


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Julio Torres Tello

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https://orcid.org/0000-0001-9694-6578

 

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Investigador Independiente, Saskatoon, Canadá.

juliotorrest@gmail.com

 

 

 

 

 

 

 

 

 

 

 

Artículo de Investigación Científica y Tecnológica

Enviado: 15/12/2023=

Revisado: 18/01/2024

Aceptado: 10/02/2024

Publicado:22/03/2024

DOI: https://doi.org/10.33= 262/concienciadigital.v7i1.3.2964     =           =  

 

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Cítese= : <= /b>

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Torres Tello, J. (2024). La influencia del aprendizaje automático en el futuro del análisis de tiempo estático. Concie= nciaDigital, 7(1.3), 172-184. https://doi.org/10.33262/conci= enciadigital.v7i1.3.2964<= /o:p>

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CONCIENCIA DIGITAL, es una revista multidisciplinar= , trimestral, que se publicará en soporte electrónico tiene como misión contribu= ir a la   formación de profesionales competentes con visión humaníst= ica y crítica que sean capaces de exponer sus resultados investigativos y científicos en la misma medida que se promueva mediante su intervención cambios positivos en la sociedad. https://concienciadigital.org  

La revista es editada por la Editorial Ciencia Digital (Editorial de prestigio registrada en la Cámara Ecuatoriana de Libro con = No de Afiliación 663) www.celibro.org.= ec

 

 

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Esta revista está protegida ba= jo una licencia Creative Commons AttributionNonCommercialNoDerivatives 4.0 International. Copia de la licencia: http://creativecommons.org/licenses/by-n= c-nd/4.0/

 

Palabr= as claves: Aprendizaje automático, Análisis de tiempo estático,

Cara= cterización, Automatización del diseño electrónico, Semiconductores

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Resumen

Introducción. El Análisis de Tiempo Estático (ATS), fundamenta= l en el diseño de circuitos integrados, implica evaluar el rendimiento tempora= l de circuitos digitales bajo diversas condiciones para cumplir ciertas restricciones, mediante simulación. A pesar de su importancia, el ATS tradicional enfrenta varias limitaciones a la hora de considerar en sus modelos la creciente complejidad del proceso de fabricación de circuitos integrados. La inclusión de Inteligencia Artificial (IA) se vislumbra como una solución prometedora para mejorar la precisión y eficiencia del ATS, reduciendo así los ciclos de diseño en la industria electrónica. Objet= ivo.  Estudiar la influencia que tiene = en la actualidad, y que puede tener a futuro la inclusión de la IA para la optimización del ATS, y por lo tanto para reducir los ciclos de diseño en= la industria de la electrónica. Metodología.  La IA se ha integrado en el Análisis = de Tiempo Estático (ATS), mejorando la precisión y eficiencia al estimar retrasos, modelar variaciones de proceso y optimizar rutas y procesos de síntesis. Esta integración permite abordar la complejidad y variabilidad = de los circuitos integrados modernos, acelerando la convergencia del diseño, reduciendo iteraciones y mejorando la calidad del diseño. Además, IA se aplica en la caracterización del modelo para ATS, utilizando simulaciones adaptativas para acelerar el proceso de verificación y reducir significativamente el tiempo de comercialización, crucial en la industria= de semiconductores. En este artículo se hace una revisión del estado actual y las proyecciones a futuro del aporte de la IA en el ATS. Discusión. El futuro del ATS promete una serie de avances que buscan mejorar sus capacidades y abordar los desafíos emergentes en el diseño de circuitos integrados. Estos desarrollos incluyen una mayor integración con el Aprendizaje Automático (ML) para mejorar la precisión y eficiencia. Con la evolución hacia nodos de proceso más pequeños, el ATS deberá adaptarse pa= ra manejar la mayor complejidad y variabilidad introducida, posiblemente empleando algoritmos de ML más sofisticados. Además, se espera que el ATS= se enfoque más en consideraciones de potencia y confiabilidad, incorporando métricas adicionales y análisis de datos más complejos, posiblemente con = la ayuda de la IA, para garantizar la eficiencia energética y la robustez co= ntra problemas de confiabilidad. Conclusión.  El futuro del ATS se perfila hacia una constante innovación y adaptación para satisfacer las cambiantes necesidades de la industria de semiconductores. Aprovechando avances tecnológicos y metodológicos, el ATS jugará un papel crucial en asegurar la entrega puntual de diseños de circuitos integrados de alto rendimiento y fiabilidad. Dada la capacidad = de optimización y análisis de datos de la IA, su potencial revolucionario en= el ATS es considerable, especialmente ante la creciente inclusión de requisi= tos cada vez más exigentes. Área de la ciencia: ingeniería electrónica= .

 

 

Keywords:

Machine learning, Static timing analysis, Characterization, Electro= nic design automation, Semiconductors<= /o:p>

 

Abstract<= /p>

Introduction. Static Timing Analysis (STA), fundamental in the design of integrated circuits, involves evaluating the temporal performance of digital circuits under various conditions to meet certain constraints, through simulation. Despi= te its importance, traditional ATS faces several limitations when considering the increasing complexity of the integrated circuit manufacturing process= in its models. The inclusion of Artificial Intelligence (AI) is seen as a promising solution to improve the precision and efficiency of the ATS, th= us reducing design cycles in the electronics industry. Objective. To study the influence that the inclusion of AI has, and may have in the fut= ure, for the optimization of the ATS, and therefore to reduce design cycles in= the electronics industry. Methodology. AI has been integrated into Sta= tic Time Analysis (STA), improving accuracy and efficiency when estimating delays, modeling process variations, and optimizing routes and synthesis processes. This integration addresses the complexity and variability of modern integrated circuits, accelerating design convergence, reducing iterations, and improving design quality. Additionally, AI is applied in model characterization for ATS, using adaptive simulations to accelerate = the verification process and significantly reduce time to market, crucial in = the semiconductor industry. This article reviews the current state and future projections of the contribution of AI in the ATS. Discussion. The future of STA promises a series of advances that seek to improve its capabilities and address emerging challenges in integrated circuit design. These developments include greater integration with Machine Learning (ML)= to improve accuracy and efficiency. With the evolution towards smaller proce= ss nodes, STA will need to adapt to manage the increased complexity and variability introduced, employing more sophisticated ML algorithms. Additionally, STA is expected to focus more on power and reliability considerations, incorporating additional metrics and more complex data analysis, with the help of AI, to ensure energy efficiency and robustness against reliability issues. Conclusion. The future of ATS is shapi= ng up to be constant innovation and adaptation to meet the changing needs of= the semiconductor industry. Technological and methodological advances will pl= ay a crucial role in ensuring the timely delivery of high-performance and reli= able integrated circuit designs. Given AI's data analysis and optimization capabilities, its revolutionary potential in ATS is considerable, especia= lly with the growing inclusion of increasingly demanding requirements.

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Introducción

El Análisis de Tiempo Estático (ATS, o STA por s= us siglas en inglés), es un paso crucial en el proceso de diseño de circuitos integrados (CI) dentro del campo de la automatización del diseño electrónico (EDA, por sus siglas en inglés). Implica evaluar el rendimiento temporal de= un circuito digital bajo diversas condiciones para asegurar que cumpla con cie= rtas restricciones. El objetivo principal del ATS es determinar si el circuito o= pera correctamente con respecto a los requisitos de tiempo, como el tiempo de establecimiento, el tiempo de retención, el retardo de reloj a salida y la frecuencia de operación máxima. Estas restricciones son críticas para el funcionamiento adecuado de los circuitos digitales y deben cumplirse para evitar problemas como violaciones de establecimiento y retención, que pueden provocar un funcionamiento incorrecto o poco fiable del circuito (Blaauw et al., 2008; Forzan & Pandini, 2009).

El ATS funciona mediante la simulación del comportamiento del circuito utilizando modelos matemáticos para predecir los retardos de propagación de las señales a través de diversas rutas dentro del diseño. Este análisis considera factores como los retardos introducidos por= las compuertas, los retardos de interconexión, variaciones de la señal de reloj= y las variaciones ambientales para evaluar con precisión el rendimiento tempo= ral del circuito (Muthukrishnan & Sathasivam, 2022).

El proceso que sigue este análisis generalmente implica los siguientes pasos (Kaeslin, 2015): (1) Generación de la lista de redes: el diseño del circuito se representa como una lista de redes, que es= una descripción de las interconexiones entre los diversos componentes (como compuertas, flip-flops e interconexiones) en el circuito. (2) Definición de= restricciones: las restricciones temporales se especifican por el diseñador en función de = los requisitos del diseño y la tecnología objetivo. Estas restricciones incluyen parámetros como frecuencias de reloj, tiempos de llegada de entrada, tiempo= s de salida requeridos y rutas máximas de retardo. (3) Análisis de tiempo: el analizador de tiempo calcula los tiempos de llegada y los tiempos requerido= s de las señales en diferentes puntos del circuito, considerando los efectos de = las compuertas lógicas, las interconexiones y las señales de reloj. Identifica = las rutas críticas y verifica si cumplen con las restricciones temporales especificadas. (4) Verificación de restricciones: los resultados del anális= is de tiempo se comparan con las restricciones temporales especificadas para identificar posibles violaciones. Si se encuentran violaciones, el diseñador puede necesitar revisar el diseño o ajustar las restricciones para lograr el cierre temporal. (5) Optimización: se pueden aplicar técnicas como la inser= ción de buffers, la síntesis del árbol de reloj y el dimensionamiento de compuer= tas para optimizar el rendimiento temporal del circuito y eliminar violaciones temporales.

Este análisis es esencial para garantizar el funcionamiento confiable de los circuitos digitales y desempeña un papel vi= tal en el proceso general de diseño de circuitos integrados. Permite a los diseñadores identificar y abordar problemas temporales temprano en el ciclo= de diseño, reduciendo el riesgo de errores costosos y asegurando que el produc= to final cumpla con los requisitos de rendimiento (Design & Reuse, s.f.).

El ATS ha sido fundamental en el diseño de chips= de VLSI desde la década de 1990, sirviendo como una herramienta crucial para la verificación temporal y facilitando la optimización del tiempo. A pesar de = sus ventajas, como la escalabilidad lineal en tiempo de ejecución y la estimaci= ón conservadora de retrasos, el ATS Determinista tradicional (DSTA) enfrenta limitaciones para modelar con precisión las variaciones de proceso, especialmente las variaciones dentro del dado, que se han vuelto significat= ivas con el escalado avance de procesos. Esta insuficiencia conduce a desafíos p= ara estimar los retrasos del circuito de manera efectiva. El Análisis Estadísti= co de Tiempo Estático (SSTA) surge como una solución, con el objetivo de abord= ar las deficiencias del DSTA al proporcionar métodos más precisos y eficientes para modelar las variaciones de proceso. Los últimos años han visto un aume= nto en la investigación centrada en SSTA, destacando su importancia para superar las limitaciones del DSTA y mejorar la precisión del análisis temporal en el diseño de IC moderno (Blaauw et al., 2008; Forzan & Pandini, 2009).

A medida que las nuevas tecnologías de silicio siguen reduciendo el tamaño del transistor, se vuelve cada vez más difícil controlar con precisión los parámetros del proceso durante la fabricación. = Como consecuencia, tanto el número como la magnitud de las fuentes independiente= s de variaciones están aumentando. Estas fluctuaciones inevitables de los paráme= tros del proceso pueden afectar significativamente el rendimiento del diseño, lo= que a menudo resulta en una pérdida considerable de rendimiento paramétrico (Fo= rzan & Pandini, 2009).

Por otro lado, nuevos modelos basados en Inteligen= cia Artificial (IA) se han aplicado con éxito en muchos campos, como la medicina (Chae et al., 2020; Jiang et al., 2018), el transporte (Castro-Zunti et al., 2020) y la seguridad (Torres-Tello et al., 2020), principalmente porque pue= den descubrir eficazmente estructuras complejas en grandes conjuntos de datos (= Han et al., 2019). La industria EDA también se puede beneficiar de esta tecnolo= gía.

En este artículo, se estudia la influencia que t= iene en la actualidad, y que puede tener a futuro la inclusión de la IA para la optimización del ATS, y por lo tanto para reducir los ciclos de diseño en la industria de la electrónica.

Metodología

La IA es un concepto tan antiguo como las propias computadoras, y vio sus primeros avances reales a mediados del siglo XX (Chollet, 2017; Raschka, 2015), cuando algunos científicos intentaron repli= car matemática y electrónicamente la comprensión básica que estaba disponible en ese momento sobre cómo funcionan las neuronas el cerebro humano. Sin embarg= o, en sus inicios, los enfoques de IA más exitosos consistían en lo que se lla= ma IA simbólica, o la idea de que la inteligencia se podía lograr programando = un conjunto de reglas suficientemente grande. Este enfoque carecía de flexibil= idad y ahora ha sido reemplazado casi por completo por el aprendizaje automático= (ML por sus siglas en inglés), un subcampo de la IA (figura 1), que utiliza algoritmos de aprendizaje para extraer información de los datos con el fin = de hacer predicciones, en lugar de que los humanos intenten derivar reglas complicadas (Raschka, 2015).

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Figura 1<= o:p>


El aprendizaje automático, o aprendizaje de máquin= a, como parte de la inteligencia artificial

Hay muchas definiciones de ML en la literatura; sin embargo, todos coinciden en la idea básica de que las máquinas puedan apren= der de los datos (Chollet, 2017; Géron, 2019; Goodfellow et al., 2016; Raschka, 2015). Algunos también señalan que se trata principalmente de un problema de ingeniería (Géron, 2019), debido a sus fundamentos estadísticos, a su gran dependencia de los ordenadores y a sus intervalos de confianza relajados (Goodfellow et al., 2016). Esa naturaleza altamente orientada a la aplicaci= ón la convierte en una ciencia, pero también en un arte (Géron, 2019; Raschka, 2015). Todo esto tiene algunas implicaciones; el más importante es un cambi= o de paradigma en la programación, que se visualiza mejor en la figura 2. En la programación clásica, un humano tiene que escribir un conjunto de reglas que operan sobre datos para proporcionar algunas respuestas, mientras que, con = ML, una computadora usa los datos y las respuestas conocidas para derivar un conjunto de reglas que luego pueden operar sobre nuevos datos para generar respuestas originales (Chollet, 2017). Este proceso, por supuesto, todavía requiere que los humanos escriban un programa. El cambio principal es que l= os programadores ahora no necesitan codificar las reglas, sino que deben especificar un espacio de modelo limitado dentro del cual la computadora necesita encontrar un modelo óptimo que se ajuste a los datos, con la ayuda= de una señal de retroalimentación (Chollet, 2017). Este proceso se llama entrenamiento.

 

 

 

 

 

Figura 2<= /span>


Aprendizaje automáti= co, un nievo paradigma de programación

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El aprendizaje automático se ha integrado cada vez= más en el ATS para mejorar su precisión, eficiencia y escalabilidad. Los algori= tmos de ML se utilizan para varios propósitos dentro del ATS, incluida la estima= ción de retraso, el análisis de tiempo estadístico (SSTA), la identificación y optimización de rutas, la síntesis de árboles de reloj y la predicción y corrección de violaciones de tiempo.

Las técnicas de ML permiten una predicción más pre= cisa de los valores de retraso en circuitos digitales al aprender de vastas cantidades de datos, proporcionando estimaciones más precisas en comparación con los métodos analíticos tradicionales. En SSTA, los modelos de ML se entrenan para modelar variaciones de proceso y otras fuentes de incertidumb= re, lo que permite una comprensión más completa del rendimiento del circuito ba= jo condiciones variables. Los algoritmos de ML también se emplean para identif= icar rutas de tiempo críticas en diseños complejos de manera más eficiente, priorizar rutas para la optimización y optimizar los procesos de síntesis de árboles de reloj mediante la predicción del sesgo y la variación del reloj, minimizando los retrasos de distribución del reloj y reduciendo el consumo = de energía. Además, ML puede predecir posibles violaciones de tiempo temprano = en el ciclo de diseño y recomendar acciones correctivas para mitigar estas violaciones, mejorando así el cierre del tiempo y la calidad general del diseño.

En general, la integración del aprendizaje automát= ico en el análisis de tiempo estático permite a los diseñadores abordar la creciente complejidad y variabilidad de los circuitos integrados modernos de manera más efectiva, lo que conduce a una convergencia de diseño más rápida, reducción de iteraciones de diseño y mejora de la calidad general del diseñ= o.

Para garantizar el funcionamiento óptimo de nuevos diseños o procesos en semiconductores, es crucial verificar todos los componentes desde los bloques más básicos hasta circuitos personalizados a través de simulaciones, considerando variables como voltaje y temperatura de operación y variaciones locales del dispositivo. Sin embargo, esta tarea es enormemente exigente, especialmente para alcanzar estándares de verificación rigurosos como los que demanda la tecnología actual, que requiere una canti= dad masiva de muestras para obtener resultados con alta confianza estadística. = La caracterización del modelo para ATS también implica un gran número de simulaciones, necesario para análisis de tiempos, potencia y área en diseños digitales. En la actualidad han aparecido innovaciones que usan ML para acelerar este proceso (Tan & Santarini, 2023), manteniendo la precisión= de la producción. Esta tecnología utiliza simulaciones adaptativas para obtener resultados iniciales, selecciona inteligentemente puntos de muestra y ajusta los resultados para obtener mediciones precisas, permitiendo alcanzar resultados de producción con una velocidad notablemente mayor que los métod= os tradicionales. Esta tecnología permite a los equipos de diseño reducir significativamente el tiempo de comercialización.<= /p>

La figura 3 muestra un ejemplo de las diferentes r= utas que una señal electrónica puede tomar dentro de un circuito. Dada la complejidad de los diseños actuales, es prácticamente imposible considerar todos los caminos al mismo tiempo, y en especial si se considera las variaciones tanto en procesos de fabricación como en condiciones de operaci= ón. Sin la ayuda de herramientas de ML, los resultados de estos análisis carece= rían de confiabilidad, y dada la creciente complejidad, es urgente encontrar herramientas que impliquen enfoques todavía más holísticos y que sean capac= es de optimizar aún más las simulaciones y control de errores.

Figura 3<= o:p>

Rutas críticas de tiempos. Cada red lógica representa una red lógica combinaciona= l, que introduce su propio retardo diferente a los demás=


Fuente: Synopsys (s.f.)

 

Discusión

El futuro del ATS ofrece varios desarrollos posibles que se espera mejoren sus capacidades y aborden los desafíos emergentes en el diseño de circuitos integrados (CI). = Algunos aspectos clave del futuro de ATS incluyen:

Mayor integración = con aprendizaje automático: es probable que = ATS continúe integrando técnicas de aprendizaje automático para mejorar la precisión y eficiencia. Los algoritmos de ML pueden ayudar a modelar mejor = las variaciones de proceso, optimizar las rutas de tiempo y predecir violacione= s de tiempo, lo que finalmente conducirá a un cierre de diseño más rápido y una mejor calidad de resultados.

Nodos de proceso avanzados: con el avance de= la tecnología de semiconductores hacia nodos de proceso más pequeños, el ATS deberá adaptarse para manejar la mayor complejidad y variabilidad introduci= da por factores como las variaciones de proceso y los efectos de interconexión. Las futuras herramientas de ATS se deben centrar en desarrollar modelos y algoritmos más sofisticados, como ML, por ejemplo, para analizar el tiempo = en estos nodos de proceso avanzados.

Tiempo para tecnol= ogías emergentes: a medida que nue= vas tecnologías como la fotónica de silicio, la integración en 3D y la computac= ión cuántica se vuelven más comunes, ATS tendrá que evolucionar para admitir el análisis de tiempo para estas tecnologías emergentes. Esto puede implicar el desarrollo de nuevos modelos y metodologías adaptadas a las características únicas de estas tecnologías.

Colaboración inter= disciplinaria: ATS implicará cada vez más la colaboración con ot= ros campos como el diseño de circuitos, el diseño físico y la fabricación. Una integración más estrecha entre el análisis de tiempo y otras etapas del flu= jo de diseño permitirá una optimización más holística y una mejor calidad gene= ral del diseño. Algoritmos de ML pueden incluirse en todo el proceso de diseño,= y a su vez estos podrían lograr optimizaciones a diferentes niveles.=

Enfoque en potenci= a y confiabilidad;<= span style=3D'font-size:12.0pt;line-height:115%;font-family:"Times New Roman",se= rif; mso-fareast-font-family:Calibri;mso-ansi-language:ES-EC'> además de las métricas de tiempo tradicionales, es probable que las futuras herramientas = de STA pongan más énfasis en el consumo de energía y las consideraciones de confiabilidad. Esto incluye analizar el tiempo bajo diferentes modos de potencia, optimizar la eficiencia energética y garantizar la robustez contra problemas de confiabilidad como el envejecimiento y la variabilidad. Esta g= ran cantidad de datos puede ser imposible de analizar sin la ayuda de la IA.

Conclusiones<= /b>

·   &nb= sp;     En general, el futuro del ATS se tiene que caracterizar por una continua innovación y adaptación para satisfacer las necesidades en evolución de la industria de semiconductores. Al aprovechar = los avances en tecnología y metodologías, el ATS desempeñará un papel vital en garantizar la entrega oportuna de diseños de CI de alto rendimiento y confiables.

·   &nb= sp;     En las últimas décadas, la IA ha encontrado aplicaciones en muchas áreas, incluyendo el diseño electrónico. Sin embargo= , la especificidad requerida en este caso hace que el desarrollo de herramientas= IA en esta aplicación, sea todavía limitada.

·&nb= sp;        Dado el potencial de optimización y análisis de da= tos que brinda la IA, esta puede tener un impacto revolucionario en el ATS, en especial si consideramos la creciente inclusión de requisitos cada vez más demandantes.

Conflicto de intereses

El autor declara que no existe conflicto de intere= ses en relación con el artículo presentado.

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Tan, W.-L., & Santarini, M. (2023). How Siemens EDA’s Solid achieved production-grade AI in EDA applications. Siemens Digital Industries Software. https://resources.sw.siemens.com/en-US/white-paper-how-siemens-edas-solido-= achieved-production-grade-ai-in-eda-applications

Torres-Tello, J., Guamán, A. V., & Ko, S.-B. (2020). Improving the detection of explosives in MOX chemical sensors array with LSTM Networks. <= i>IEEE Sensors Journal, 20(23): 14302–14309. https://doi.org/10.1109/JSEN.2020.3007431

Synopsys. (s.f.). What is Static Timing Analysis (STA)? – How STA works? https://www.synopsys.com/glossary/what= -is-static-timing-analysis.html

 

 

 

 

 

 

 

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