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Modeling Analog-Digital-Converter Energy and Area for Compute-In-Memory Accelerator Design

Core Concepts
Analog Compute-in-Memory (CiM) accelerators use analog-digital converters (ADCs) to read the analog values they compute. This work presents an open-source architecture-level model to estimate ADC energy and area, enabling researchers to quickly and easily model key architecture-level tradeoffs in accelerators that use ADCs.
The content presents an architecture-level model to estimate the energy and area of analog-digital converters (ADCs) used in Compute-in-Memory (CiM) accelerators. The model takes four input parameters: number of ADCs, total throughput, technology node, and ADC resolution (effective number of bits, ENOB). The energy model uses two throughput-dependent bounds to estimate best-case ADC energy, which also depends on ENOB and technology node. The area model uses regression over throughput, ENOB, technology node, and energy to estimate ADC area, with further optimization to match the lowest-area 10% of published ADCs. The authors open-source the model and integrate it into the CiM modeling tool CiMLoop, using it to model the CiM architecture RAELLA. They demonstrate how the model can be used to explore the impact of ADC design choices on full-accelerator energy and area. For example, they show that summing more analog values and using higher-ENOB ADCs can reduce energy in high-utilization DNN layers, and that the optimal number of ADCs depends on the required throughput to minimize the energy-area product.
Analog Compute-In-Memory (CiM) accelerators can consume significant energy and area due to the analog-digital converters (ADCs) used to read computed analog values. ADC energy and area can vary by orders-of-magnitude even for ADCs with the same architecture-level parameters. The model estimates best-case ADC energy using two throughput-dependent bounds that also depend on ENOB and technology node. The model estimates ADC area using regression over throughput, ENOB, technology node, and energy, with further optimization to match the lowest-area 10% of published ADCs.
"ADCs can consume significant energy and area, and architecture-level decisions such as ADC resolution or number of ADCs can impact the ADC energy and area by orders-of-magnitude [1]." "For this reason, architecture-level ADC decisions can significantly affect full-accelerator energy and area [2]–[4]."

Deeper Inquiries

How could the model be extended to capture additional factors that influence ADC energy and area, such as microarchitectural design choices or specific circuit-level techniques?

To enhance the model's capability to consider microarchitectural design choices and specific circuit-level techniques, several extensions could be implemented. Firstly, incorporating detailed circuit-level simulations to capture the impact of specific design choices on ADC energy and area would provide a more granular analysis. This could involve modeling transistor-level characteristics, such as transistor sizing, layout optimization, and parasitic effects, to better reflect the intricacies of the ADC design. Additionally, integrating factors like clock distribution, signal routing, and power delivery mechanisms into the model would offer a more comprehensive view of the ADC's energy and area requirements. Furthermore, including considerations for technology-specific effects, such as process variations, temperature dependencies, and aging effects, would make the model more robust and adaptable to different fabrication technologies.

What are the potential limitations or drawbacks of using a purely architecture-level model, and how could the model be combined with more detailed circuit-level simulations to provide a more comprehensive analysis?

While an architecture-level model provides a high-level overview of ADC energy and area, it may overlook intricate details that significantly impact the final design. One limitation is the lack of accuracy in capturing the nuances of circuit-level optimizations and trade-offs that can affect energy and area metrics. By combining the architecture-level model with detailed circuit-level simulations, a more comprehensive analysis can be achieved. This hybrid approach would enable the model to leverage the strengths of both levels of abstraction. The architecture-level model could serve as a rapid exploration tool for evaluating high-level design decisions, while the circuit-level simulations could offer precise insights into the effects of specific design choices on ADC performance. By integrating these two approaches, designers can benefit from a holistic view of the ADC design space, balancing speed and accuracy in the evaluation process.

How might the insights from this work on ADC modeling be applied to the design of other types of analog components or mixed-signal circuits in emerging computing architectures?

The insights gained from ADC modeling can be extrapolated to the design of various analog components and mixed-signal circuits in emerging computing architectures. By understanding the trade-offs between energy, area, resolution, and throughput in ADCs, similar principles can be applied to the design of other analog blocks, such as digital-to-analog converters (DACs), voltage references, amplifiers, and filters. The modeling framework developed for ADCs can be adapted to analyze the energy and area characteristics of these components, enabling designers to make informed decisions during the architectural design phase. Moreover, the methodologies and techniques employed in ADC modeling, such as regression analysis, energy bounds estimation, and area scaling, can be extended to optimize the performance of mixed-signal circuits in novel computing paradigms like neuromorphic computing, quantum computing, and edge computing. This cross-application of insights can streamline the design process and facilitate the development of efficient and high-performance analog and mixed-signal circuits in diverse computing applications.