TAFA: Design Automation of Analog Mixed-Signal FIR Filters Using Time Approximation Architecture

• URL: http://arxiv.org/abs/2112.07825v1
• Date: Wed, 15 Dec 2021 01:47:35 GMT
• Title: TAFA: Design Automation of Analog Mixed-Signal FIR Filters Using Time Approximation Architecture
• Authors: Shiyu Su, Qiaochu Zhang, Juzheng Liu, Mohsen Hassanpourghadi, Rezwan Rasul, and Mike Shuo-Wei Chen
• Abstract summary: A digital finite impulse response (FIR) filter design is fully synthesizable, thanks to the mature CAD support of digital circuitry. analog mixed-signal (AMS) filter design is mostly a manual process, including architecture selection, schematic design, and layout. This work presents a systematic design methodology to automate AMS FIR filter design using a time approximation architecture.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A digital finite impulse response (FIR) filter design is fully synthesizable, thanks to the mature CAD support of digital circuitry. On the contrary, analog mixed-signal (AMS) filter design is mostly a manual process, including architecture selection, schematic design, and layout. This work presents a systematic design methodology to automate AMS FIR filter design using a time approximation architecture without any tunable passive component, such as switched capacitor or resistor. It not only enhances the flexibility of the filter but also facilitates design automation with reduced analog complexity. The proposed design flow features a hybrid approximation scheme that automatically optimize the filter's impulse response in light of time quantization effects, which shows significant performance improvement with minimum designer's efforts in the loop. Additionally, a layout-aware regression model based on an artificial neural network (ANN), in combination with gradient-based search algorithm, is used to automate and expedite the filter design. With the proposed framework, we demonstrate rapid synthesis of AMS FIR filters in 65nm process from specification to layout.

Related papers

• LMS-AutoTSF: Learnable Multi-Scale Decomposition and Integrated Autocorrelation for Time Series Forecasting [4.075971633195745]
  We introduce LMS-AutoTSF, a novel time series forecasting architecture that incorporates autocorrelation. Unlike models that rely on predefined trend and seasonal components, LMS-AutoTSF employs two separate encoders per scale. A key innovation in our approach is the integration of autocorrelation, achieved by computing lagged differences in time steps.
  arXiv  Detail & Related papers  (2024-12-09T09:31:58Z)
• STAR: Synthesis of Tailored Architectures [61.080157488857516]
  We propose a new approach for the synthesis of tailored architectures (STAR) Our approach combines a novel search space based on the theory of linear input-varying systems, supporting a hierarchical numerical encoding into architecture genomes. STAR genomes are automatically refined and recombined with gradient-free, evolutionary algorithms to optimize for multiple model quality and efficiency metrics. Using STAR, we optimize large populations of new architectures, leveraging diverse computational units and interconnection patterns, improving over highly-optimized Transformers and striped hybrid models on the frontier of quality, parameter size, and inference cache for autoregressive language modeling.
  arXiv  Detail & Related papers  (2024-11-26T18:42:42Z)
• MATADOR: Automated System-on-Chip Tsetlin Machine Design Generation for Edge Applications [0.2663045001864042]
  This paper presents MATADOR, an automated-to-silicon tool with GUI interface capable of optimized accelerator design for inference at the edge. It offers automation of the full development pipeline: model training, system level design generation, design verification and deployment. MATADOR accelerator designs are shown to be up to 13.4x faster, up to 7x more resource frugal and up to 2x more power efficient when compared to state-of-the-art Quantized and Binary Deep Neural Network implementations.
  arXiv  Detail & Related papers  (2024-03-03T10:31:46Z)
• Automated Design and Optimization of Distributed Filtering Circuits via Reinforcement Learning [20.500468654567033]
  This study proposes a novel end-to-end automated method for DFC design. The proposed method harnesses reinforcement learning (RL) algorithms, eliminating the dependence on the design experience of engineers. Our method achieves superior performance when designing complex or rapidly evolving DFCs.
  arXiv  Detail & Related papers  (2024-02-22T02:36:14Z)
• Compositional Generative Inverse Design [69.22782875567547]
  Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem. We show that by instead optimizing over the learned energy function captured by the diffusion model, we can avoid such adversarial examples. In an N-body interaction task and a challenging 2D multi-airfoil design task, we demonstrate that by composing the learned diffusion model at test time, our method allows us to design initial states and boundary shapes.
  arXiv  Detail & Related papers  (2024-01-24T01:33:39Z)
• CktGNN: Circuit Graph Neural Network for Electronic Design Automation [67.29634073660239]
  This paper presents a Circuit Graph Neural Network (CktGNN) that simultaneously automates the circuit topology generation and device sizing. We introduce Open Circuit Benchmark (OCB), an open-sourced dataset that contains $10$K distinct operational amplifiers. Our work paves the way toward a learning-based open-sourced design automation for analog circuits.
  arXiv  Detail & Related papers  (2023-08-31T02:20:25Z)
• Filter Pruning for Efficient CNNs via Knowledge-driven Differential Filter Sampler [103.97487121678276]
  Filter pruning simultaneously accelerates the computation and reduces the memory overhead of CNNs. We propose a novel Knowledge-driven Differential Filter Sampler(KDFS) with Masked Filter Modeling(MFM) framework for filter pruning.
  arXiv  Detail & Related papers  (2023-07-01T02:28:41Z)
• RobustAnalog: Fast Variation-Aware Analog Circuit Design Via Multi-task RL [19.71047877921737]
  We present Robust Analog, a robust circuit design framework that involves the variation information in the optimization process. We show that Robust Analog can significantly reduce required optimization time by 14-30 times.
  arXiv  Detail & Related papers  (2022-07-13T04:06:32Z)
• Squeezeformer: An Efficient Transformer for Automatic Speech Recognition [99.349598600887]
  Conformer is the de facto backbone model for various downstream speech tasks based on its hybrid attention-convolution architecture. We propose the Squeezeformer model, which consistently outperforms the state-of-the-art ASR models under the same training schemes.
  arXiv  Detail & Related papers  (2022-06-02T06:06:29Z)
• In-filter Computing For Designing Ultra-light Acoustic Pattern Recognizers [6.335302509003343]
  We present a novel in-filter computing framework that can be used for designing ultra-light acoustic classifiers. The proposed architecture integrates the convolution and nonlinear filtering operations directly into the kernels of a Support Vector Machine. We show that the system can achieve robust classification performance on benchmark sound recognition tasks using only 1.5k Look-Up Tables (LUTs) and 2.8k Flip-Flops (FFs)
  arXiv  Detail & Related papers  (2021-09-11T08:16:53Z)
• Unsharp Mask Guided Filtering [53.14430987860308]
  The goal of this paper is guided image filtering, which emphasizes the importance of structure transfer during filtering. We propose a new and simplified formulation of the guided filter inspired by unsharp masking. Our formulation enjoys a filtering prior to a low-pass filter and enables explicit structure transfer by estimating a single coefficient.
  arXiv  Detail & Related papers  (2021-06-02T19:15:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.