Electronic Design Automation Tools Research Articles

Enhancing Electronic Design Automation Tools with an ML-Based Information Retrieval System

Over the past fifty years, Electronic Design Automation (EDA) tools have played a crucial role in the semiconductor industry, assisting in the design, simulation, and manufacturing of integrated circuits (ICs). However, the sophisticated nature of these tools often demands extensive expertise, which can be a barrier for many users. Mastery of these tools necessitates specialized knowledge and skills, including comprehension of complex algorithms, design methodologies, and tool-specific workflows. To address this challenge, this paper introduces a machine learning (ML) based information retrieval system designed to enhance the usability of EDA tools. The objective of this system is to simplify user interactions and make EDA tools more accessible to designers, regardless of their expertise level. The main idea of this ML-driven system is to provide a chatbot-like interface that facilitates efficient, context-aware searches and offers interactive, step-by-step guidance on using various tool functionalities. By integrating natural language processing and machine learning techniques, the system can understand user queries, extract relevant information from the tool's documentation, and provide context-specific guidance. This approach helps to mitigate the steep learning curve associated with advanced EDA applications and enhances tool accessibility. Consequently, it promotes a more intuitive interaction with sophisticated EDA software, thus fostering enhanced usability of complex tools in the semiconductor industry. This work exemplifies the transformative potential of integrating machine learning with conversational user interfaces in making sophisticated software applications more user-friendly.

Enhancing Electronic Design Automation Tools with an ML-Based Information Retrieval System

Over the past fifty years, Electronic Design Automation (EDA) tools have played a crucial role in the semiconductor industry, assisting in the design, simulation, and manufacturing of integrated circuits (ICs). However, the sophisticated nature of these tools often demands extensive expertise, which can be a barrier for many users. Mastery of these tools necessitates specialized knowledge and skills, including comprehension of complex algorithms, design methodologies, and tool-specific workflows. To address this challenge, this paper introduces a machine learning (ML) based information retrieval system designed to enhance the usability of EDA tools. The objective of this system is to simplify user interactions and make EDA tools more accessible to designers, regardless of their expertise level. The main idea of this ML-driven system is to provide a chatbot-like interface that facilitates efficient, context-aware searches and offers interactive, step-by-step guidance on using various tool functionalities. By integrating natural language processing and machine learning techniques, the system can understand user queries, extract relevant information from the tool's documentation, and provide context-specific guidance. This approach helps to mitigate the steep learning curve associated with advanced EDA applications and enhances tool accessibility. Consequently, it promotes a more intuitive interaction with sophisticated EDA software, thus fostering enhanced usability of complex tools in the semiconductor industry. This work exemplifies the transformative potential of integrating machine learning with conversational user interfaces in making sophisticated software applications more user-friendly.

Squaring Circuit Using 14nmFinFET Technology with Vedic Mathematics Approach

FinFET technology is an alternative to CMOS technology as in this technology device has higher drive current, speed, and low-power consumption. FinFET has better mobility and scaling than CMOS technology. A dedicated Squaring circuit is needed by many digital signal processors (DSP) and arithmetic and logical units (ALU). So fast, power-efficient, and compact squaring circuits can be designed with Vedic sutras. Vedic mathematics has 16 sutras and 13 sub-sutras that touch almost all different chapters of mathematics. Out of these sutras, Dwandwayoga has a duplex property that is used to find a square of an N-bit number. Also, Yavadunam is a special case for finding the square of a number that is closer to the base. This work targets a comparative analysis of these two square circuits having a Vedic approach with a conventional array multiplier. The proposed designs are implemented in micro-wind 3.9 electronic design automation tool (EDA) with 14 nm deep submicron technology post-layout. The values of model parameters are used from the current Berkeley 4 short channel model (BSIM4). It is observed that the proposed square architecture design using the Dwandwayog sutra and Yavadunam sutra achieves 185%, 272.45% delay depletion and a 122.22%, and 46.52% reduction in transistor requirements compared to the conventional array multiplier, respectively.

Design and Test of Offset Quadrature Phase-Shift Keying Modulator with GF180MCU Open Source Process Design Kit

This article explores the evolution of integrated circuits (ICs), highlighting the fundamental role of open source Electronic Design Automation (EDA) tools in their development. It describes the IC’s design flow, differentiating between Front-end and Back-end design stages, and details the process of implementing the digital stage in offset quadrature phase-shift keying (OQPSK) modulation in an IC, including its hardware description language (HDL), the implementation test in the field-programmable gate array (FPGA), and the physical layout using the first manufactured open source process design kits (PDKs) in Global Foundries’ 180 nm, as well as the use of OpenLane and Caravel. To conclude, the results of the physical tests obtained from the digital modulation are presented, as well as the performance of the raised cosine shaping filter.

Towards Automated RISC-V Microarchitecture Design with Reinforcement Learning

Microarchitecture determines the implementation of a microprocessor. Designing a microarchitecture to achieve better performance, power, and area (PPA) trade-off has been increasingly difficult. Previous data-driven methodologies hold inappropriate assumptions and lack more tightly coupling with expert knowledge. This paper proposes a novel reinforcement learning-based (RL) solution that addresses these limitations. With the integration of microarchitecture scaling graph, PPA preference space embedding, and proposed lightweight environment in RL, experiments using commercial electronic design automation (EDA) tools show that our method achieves an average PPA trade-off improvement of 16.03% than previous state-of-the-art approaches with 4.07× higher efficiency. The solution qualities outperform human implementations by at most 2.03× in the PPA trade-off.

In-depth Multi-Objective charge pump design space exploration towards the automatic synthesis of power management units

The inclusion of automation mechanisms in the intricate design of power management units (PMUs) is long pursued in industry. This paper addresses a mixed-signal design flow whose purpose is to automatically produce, from the system-level to the ready-for-tape-out layout, complex PMUs when given a set of specifications. Particularly, in that flow, at the IP level, the proper analysis of the design tradeoffs is tedious and impractical, as a large amount of conflicting performance figures obtained from transient analysis for typical and multiple process/temperature corners, must be considered simultaneously. Therefore, it is common for the design cycle of a single integrated charge pump required in a state-of-the-art PMU to surpass a full month, depending on the intended technology, devices’ types and operation voltages. Additionally, by the end of this process, circuit designers often end up with a limited vision of the attainable performances. However, due to the recent improvements in computational power of modern workstations, optimization-based techniques with expensive evaluation mechanisms are now perceived as a potential solution to pursue optimal sizing and layout. Therefore, an academic electronic design automation tool is adapted and applied to explore the performance boundaries of a charge pump in a 180-nm technology node and determine the optimal tradeoffs between performance and the, severely constrained, layout area. In the last step, a worst-case corner optimization on a 45- and 99- dimensional design and performance spaces, respectively, derived from 9 time-consuming transient analysis of each candidate solution, produced 35 design solutions that provide a thorough analysis between worst-case efficiency, voltage drop, and rise and fall times, impossible to perform in the manual design. The obtained insights on the design space were used to speed-up the process of devising a solution for tape-out.

Design of a 16–bit 500 MS s–1 SAR-ADC at 45 nm for low power and high frequency applications

This study gives a thorough analysis of the performance of a 45 nm CMOS process-designed 16-bit 500 MS/s successive approximation register analog-to-digital converter (SAR-ADC). 16-bit R-2R DAC double-tail dynamic latch, Widlar current method, variable body-biasing technique, sample and hold block, 16-bit SAR, and 16-bit latch are used to design proposed SAR-ADC block. The SAR-ADC design and simulations were carried out using Cadence Virtuoso software. This powerful electronic design automation (EDA) tool facilitated the design, layout, and simulation of the ADC, ensuring a comprehensive analysis of its performance characteristics. MATLAB was used for post-simulation data analysis, processing, and visualization. The proposed SAR-ADC is compared with few existing examples listed as ADS8881, LTC2380–16, ADS8344, LTC2368, and MAX11156 on the performance metrics including signal-to-noise ratio (SNR), figure of merit (FOM), total harmonic distortion (THD), resolution, delay, power consumption and figure of Merit (FOM). This work is highlighting different aspects of the suggested architecture and demonstrates how it outperforms benchmark of ADCs in terms of power usage, SNR, THD, and FOM. A SAR-ADC attains a power consumption of 39.2 μW while operating at sampling frequency of 500 MS s–1 at supply voltage of 1 V. The results provide fresh perspectives for potential improvements in existing work in terms of reduction in power consumption and high-speed ADC at 16-bit resolution and also Jitter is scrutinized across various stages of the SAR-ADC. The proposed low power, high speed and high-resolution SAR ADC is targeted for high-quality analog-to-digital signal conversion useful in industrial automation systems, medical devices, IoT and audio processing modules.

Chiplets for Integration of Electronic Systems

Scaling down of transistor sizes has made it possible to integrate billions of devices in the same chip, allowing us to build entire electronic systems into tiny “systems on chip” (SoC). For about four decades after the proclamation of Moore's law, device scaling resulted in improvements in area, performance as well as power. In the last two decades, many semiconductor manufacturers stopped investing in new technology nodes since the design, manufacturing, and test challenges in deep submicron technologies are far too steep. Today, semiconductor companies do not rely on improvements in photolithography alone to build more complex systems. Similarly, optimizations at gate level or register-transfer level through electronic design automation tools will not suffice to bring in order-of-magnitude increase in the complexity of SoC, since these tools are already mature. Therefore, optimization at the system level is the only logical step forward. Chiplets represent a system-level packaging innovation and there is much research and development in using this technology for building high-performance computing systems. We believe that it will be worthwhile to include the topic of system-level integration in a course on Very Large-Scale Integrated Circuit Design taught in the Universities and provide a tutorial treatment to help teachers and students.

Active matrix-based pressure sensor system with a 4 × 16 printed decoder designed with a flexible hybrid organic process design kit

The innovative field of printed sensor with a demand for high accuracy, sensitivity and durability has enabled a wide application area in sensing, healthcare etc. A large-area printed sensor system on a flexible foil substrate emplying p-type organic field-effect transistors (OFETs) is presented. Thereby, the OFET is fabricated through a hybrid manufacturing process, including photolithographically structured source- and drain-electrodes, ink-jet printed organic semiconductor, and spin-coated dielectric. Moreover, a dedicated device model, derived from the variable range hopping model, is developed and integrated together with process related design rules, materials properties and geometric information into a comprehensive process design kit (FH_OPDK). The FH_OPDK is integrated in a commercial electronic design automation tool and is used to design and perform post-layout simulations on logic gates, such as INV, NAND2, and NOR2 as well as circuitry such as ring oscillators and a 4 × 16 digital decoder. Several circuit topologies have been tested and evaluated in a detailed model-hardware correlation analysis. Finally we have optimized logic gates and the decoder in a PMOS only, pseudo CMOS design style. To demonstrate the feasibility of the full sensor system in hardware a 16 × 16 active matrix pressure sensor on a flexible substrate integrated with a 4 × 16 binary decoder was fabricated and tested. We have integrated our flexible hybrid sensor system with a PCB board and a microcontroller to demonstrate the hardware readout platform capable of detecting the weight of objects and visualizing a digital map of applied forces.

An Efficient Reinforcement Learning Based Framework for Exploring Logic Synthesis

Logic synthesis is a crucial step in electronic design automation tools. The rapid developments of reinforcement learning (RL) have enabled the automated exploration of logic synthesis. Existing RL based methods may lead to data inefficiency, and the exploration approaches for FPGA and ASIC technology mapping in recent works lack the flexibility of the learning process. This work proposes ESE, a reinforcement learning based framework to efficiently learn the logic synthesis process. The framework supports the modeling of logic optimization and technology mapping for FPGA and ASIC. The optimization for the execution time of the synthesis script is also considered. For the modeling of FPGA mapping, the logic optimization and technology mapping are combined to be learned in a flexible way. For the modeling of ASIC mapping, the standard cell based optimization and LUT optimization operations are incorporated into the ASIC synthesis flow. To improve the utilization of samples, the Proximal Policy Optimization model is adopted. Furthermore, the framework is enhanced by supporting MIG based synthesis exploration. Experiments show that for FPGA technology mapping on the VTR benchmark, the average LUT-Level-Product and script runtime are improved by more than 18.3% and 12.4% respectively than previous works. For ASIC mapping on the EPFL benchmark, the average Area-Delay-Product is improved by 14.5%.

Machine Learning Application for Early Power Analysis Accuracy Improvement: A Case Study for Nets Switching Power

In the quest for precise power estimation during the early phases of design, the absence of a Standard Parasitic Exchange File (SPEF) with interconnect R/C values poses a significant hurdle. To address this challenge, we introduce a Machine Learning (ML) approach designed to predict net power metrics at the Gate Level without relying on SPEF. Net features are extracted from Electronic Design Automation (EDA) tools, facilitating the training of models for the prediction of Net Switching Power. Notably, the Random Forest model emerges as the most effective, achieving high accuracy by reducing power error from around 20% to a mere 0.1%.Furthermore, our innovative approach enhances efficiency by bypassing the traditional SPEF generation process. This results in a significant 5x reduction in runtime compared to the conventional flow, with a notable decrease from 163.6 minutes to just 33.7 minutes. This substantial acceleration is achieved by skipping the time-intensive synthesis and physical design steps required for SPEF.In summary, our ML-based methodology not only achieves swift and accurate power estimation in the early stages of design but also liberates the process from the constraints of SPEF dependency. This marks a transformative shift in the landscape of power estimation methodologies.

Custom ASIC Design for SHA-256 Using Open-Source Tools

The growth of digital communications has driven the development of numerous cryptographic methods for secure data transfer and storage. The SHA-256 algorithm is a cryptographic hash function widely used for validating data authenticity, identity, and integrity. The inherent SHA-256 computational overhead has motivated the search for more efficient hardware solutions, such as application-specific integrated circuits (ASICs). This work presents a custom ASIC hardware accelerator for the SHA-256 algorithm entirely created using open-source electronic design automation tools. The integrated circuit was synthesized using SkyWater SKY130 130 nm process technology through the OpenLANE automated workflow. The proposed final design is compatible with 32-bit microcontrollers, has a total area of 104,585 µm2, and operates at a maximum clock frequency of 97.9 MHz. Several optimization configurations were tested and analyzed during the synthesis phase to enhance the performance of the final design.

A Field Programmable Gate Array Placement Methodology for Netlist-Level Circuits with GPU Acceleration

Field Programmable Gate Arrays (FPGAs), renowned for their reconfigurable nature, offer unmatched flexibility and cost-effectiveness in engineering experimentation. They stand as the quintessential platform for hardware acceleration and prototype validation. With the increasing ubiquity of FPGA chips and the escalating scale of system designs, the significance of their accompanying Electronic Design Automation (EDA) tools has never been more pronounced. The placement process, serving as the linchpin in FPGA EDA, directly influences FPGA development and operational efficiency. This paper introduces an FPGA placement methodology hinging on the Verilog-to-Routing (VTR) framework. We introduce a novel packing approach grounded in the weighted Edmonds’ Blossom algorithm, ensuring that the CLB generation strategy aligns more closely with load-balanced distribution. Furthermore, we enhanced the electric field-driven resolver placement process for CLB locations and leverage GPU-accelerated design. Experimental results demonstrate substantial improvements over the traditional VTR algorithm, with an average optimization of 28.42% in the packing process runtime, an average acceleration ratio of 2.85 times in the placement phase, and a 39.97% reduction in total packing and placement runtime consumption.

Improvement in Sizing Constrained Analog IC via Ts-CPD Algorithm

In this work, we propose a variation of the cellular particle swarm optimization algorithm with differential evolution hybridization (CPSO-DE) to include constrained optimization, named Ts-CPD. It is implemented as a kernel of electronic design automation (EDA) tool capable of sizing circuit components considering a single-objective design with restrictions and constraints. The aim is to improve the optimization solutions in the sizing of analog circuits. To evaluate our proposal’s performance, we present the design of three analog circuits: a differential amplifier, a two-stage operational amplifier (op-amp), and a folded cascode operational transconductance amplifier. Numerical simulation results indicate that Ts-CPD can find better solutions, in terms of the design objective and the accomplishment of constraints, than those reported in previous works. The Ts-CPD implementation was performed in Matlab using Ngspice and can be found on GitHub (see Data Availability Statement).

MOEA/D vs. NSGA-II: A Comprehensive Comparison for Multi/Many Objective Analog/RF Circuit Optimization through a Generic Benchmark

Thanks to the enhanced computational capacity of modern computers, even sophisticated analog/radio frequency (RF) circuit sizing problems can be solved via electronic design automation (EDA) tools. Recently, several analog/RF circuit optimization algorithms have been successfully applied to automatize the analog/RF circuit design process. Conventionally, metaheuristic algorithms are widely used in optimization process. Among various nature-inspired algorithms, evolutionary algorithms (EAs) have been more preferred due to their superiorities (robustness, efficiency, accuracy etc.) over the other algorithms. Furthermore, EAs have been diversified and several distinguished analog/RF circuit optimization approaches for single-, multi-, and many-objective problems have been reported in the literature. However, there are conflicting claims on the performance of these algorithms and no objective performance comparison has been revealed yet. In the previous work, only a few case study circuits have been under test to demonstrate the superiority of the utilized algorithm, so a limited comparison has been made for only these specific circuits. The underlying reason is that the literature lacks a generic benchmark for analog/RF circuit sizing problem. To address these issues, we propose a comprehensive comparison of the most popular two evolutionary computation algorithms, namely Non-Sorting Genetic Algorithm-II and Multi-Objective Evolutionary Algorithm based Decomposition, in this article. For that purpose, we introduce two ad hoc testbenches for analog and RF circuits including the common building blocks. The comparison has been made at both multi- and many-objective domains and the performances of algorithms have been quantitatively revealed through the well-known Pareto-optimal front quality metrics.

Shallow Clock Tree Pre-Estimation for Designing Clock Tree Synthesizable Verilog RTLs

Clock tree synthesis (CTS) is an important process in determining overall chip timing and power consumption. The CTS is also a time-consuming process for checking the clock tree. If the chip design and specification do not match, the CTS result will be wrong. Many users use licensed electronic design automation (EDA) tools like Synopsys, and Cadence to carry out accurate chip verification. However, when using a licensed EDA tool, it is difficult to change the function and confirm the overall process in detail. If the design is wrong, the expected cost is doubled, as it will be necessary to modify the design and check all processes for verification. Currently, it cannot check the synthesizability of the clock tree on the placement and route process using only RTL. The main purpose of this study is to predict the CTS result of pre-estimation roughly using an RTL source placing temporary logics using random buffer insertion before the route process: then the incorrectly designed part can be freely modified because the CTS result can be known in advance. Experimental results showed that this research achieves an increase in inserted buffer area by about 10%, the standard deviation of clock skew achieves zero clock skew after shallow CTS, and clock frequency increases by about 10%. This paper contributes to optimizing clock tree implementation by conducting the pre-route process before using the CTS tool. Also, our approach not only minimizes resource usage but also optimizes CTS for the RTL structure. It holds considerable value in enhancing the efficiency and performance of integrated circuits.

Sequential Circuits Synthesis for Rapid Single Flux Quantum Logic Based on Finite State Machine Decomposition

Rapid Single Flux Quantum (RSFQ) logic is a promising technology to supersede Complementary metal-oxidesemiconductor (CMOS) logic in some specialized areas due to providing ultra-fast and energy-efficient circuits. To realize a large-scale integration design, electronic design automation (EDA) tools specialized for RSFQ logic are required due to the divergences in logic type, timing constraints, and circuit structure compared with CMOS logic. Logic synthesis is crucial in converting behavioral circuit description into a circuit netlist, typically combining combinational and sequential circuit synthesis. For the RSFQ logic, the sequential circuit synthesis is challenging, especially for non-linear sequential blocks with feedback loops. Thus, this paper presents a sequential circuit synthesis algorithm based on finite state machine (FSM) decomposition, which ensures design functionality, lowers costs, and improves the RSFQ circuit performance. Additionally, we present the synthesis processes of the feedback logic and the 2-bit counter to demonstrate how the proposed algorithm operates, and ISCAS89 benchmark circuits reveal our method’s ability to synthesize large-scale sequential circuits.

Boosting VLSI Design Flow Parameter Tuning with Random Embedding and Multi-objective Trust-region Bayesian Optimization

Modern very large-scale integration (VLSI) design requires the implementation of integrated circuits using electronic design automation (EDA) tools. Due to the complexity of EDA algorithms, there are numerous tool parameters that have imperative impacts on the chip design quality. Manual selection of parameter values is excessively laborious and constrained by experts’ experience. Due to the high complexity and lack of parallelization, most existing parameter tuning methods cannot make sufficient exploration in a large search space. In this article, we boost the efficiency and performance of parameter tuning with random embedding and multi-objective trust-region Bayesian optimization. Random embedding can effectively cut down the number of variables in the search process and thus reduce the runtime of Bayesian optimization. Multi-objective trust-region Bayesian optimization allows the algorithm to explore diverse solutions with excellent parallelism. Due to the ability to do more exploration in limited runtime, the proposed framework can achieve better performance than existing methods in our experiments.

A Universal-Verification-Methodology-Based Testbench for the Coverage-Driven Functional Verification of an Instruction Cache Controller

The Cache plays an important role in computer architecture by reducing the access time of the processor and improving its performance. The hardware design of the Cache is complex and it is challenging to verify its functions, so the traditional Verilog-based verification method is no longer applicable. This paper proposes a comprehensive and efficient verification testbench based on the SystemVerilog language and universal verification methodology (UVM) for an instruction Cache (I-Cache) controller. Corresponding testcases are designed for each feature of the I-Cache controller and automatically executed using a python script on an electronic design automation (EDA) tool. After simulating a large number of testcases, the statistics reveal that the module’s code coverage is 99.13%. Additionally, both the function coverage and the assertion coverage of the module reach 100%. Our results demonstrate that these coverage metrics meet the requirements and ensure the thoroughness of function verification. Furthermore, the established verification testbench exhibits excellent scalability and reusability, making it easily applicable to higher-level verification scenarios.

AOS: An Automated Overclocking System for High-Performance CNN Accelerator Through Timing Delay Measurement on FPGA

With the inherent algorithmic error resilience of conventional neural networks (CNN) and the worst-case design methodologies of current electronic design automation tools, overclocking-based timing speculation is a promising technique to improve the performance of CNN accelerators on FPGA by removing unnecessary timing margins. To avoid potential timing errors, timing delay measurement should be used during overclocking. However, current approaches are not yet good at measuring paths with more intense variability factors such as jitter, and lack an automated process for testing circuit delays. In this paper, we first propose 2-dimension multi-frame fusion to deal with the sampling jitter, then present a timing delay measurement-based automatic overclocking system (AOS) running on heterogeneous FPGA for high-performance CNN accelerators. On the FPGA side, AOS is composed of timing delay monitors (TDM) that can measure all types of timing paths, a TDM controller that converts the sampled values of TDMs into timing delay in terms of the ratio of path delay to the clock period. On the CPU side, AOS converts the path delay from clock period ratio to absolute delay value and decides the frequency of the accelerator in the next iteration. We demonstrate AOS with a SkyNet accelerator on the Xilinx ZCU104 board and achieve 657FPS at 436MHz without accuracy degradation, which is 1.41× performance compared to the baseline.