A 32-channel event-based bio-signal analog front-end with adaptive delta and pulse frequency encoding

• URL: http://arxiv.org/abs/2607.12901v1
• Date: Tue, 14 Jul 2026 15:37:34 GMT
• Title: A 32-channel event-based bio-signal analog front-end with adaptive delta and pulse frequency encoding
• Abstract summary: We present an event-based AFE Application-Specific Integrated Circuit optimized for biomedical signal acquisition and encoding.<n>The proposed ASIC offers a highly interface compatible with state-of-the-art Spiking Neural Network (SNN) neuromorphic processors.
• Score: 3.194937813761537
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Low-power event-based Analog Front-Ends (AFEs) are essential for building efficient, end-to-end neuromorphic signal processing systems. In this paper, we present an event-based AFE Application-Specific Integrated Circuit (ASIC) optimized for biomedical signal acquisition and encoding. The chip features 32 independently programmable input channels with dual-mode encoding mechanism outputs, comprising Pulse Frequency Modulation (PFM) and adaptive Asynchronous Delta Modulator (aADM) circuits. The aADM encoder provides an auto-scaling mechanism that adapts the encoding data-rate based on the input signal envelope in real-time, enabling very high data compression for low-power information transmission. This approach paves the way toward adaptive wireless communication of neural signals for on-line processing in brain-computer interfaces. Fabricated in a 180 nm CMOS process, the proposed ASIC offers a highly configurable interface compatible with state-of-the-art Spiking Neural Network (SNN) neuromorphic processors.

Related papers

• Multi-layer MIMO Relay as Deep Physical Neural Networks: Power Amplifiers as Activation Functions [56.870614369768354]
  Wireless physical neural networks (WPNNs) embed neural computation directly into analog hardware.<n>We propose a deep WPNN in which nonlinear activations are realized by a multi-hop multiple-input multiple-output (MIMO) relay network.<n>We show that the proposed architecture enables accurate over-the-air inference for image classification.
  arXiv  Detail & Related papers  (2026-07-20T09:45:07Z)
• Adaptive Source-Channel Coding for Semantic Communications [48.13990936094994]
  Semantic communications (SemComs) have emerged as a promising paradigm for joint data and task-oriented transmissions.<n>Current joint source-channel coding (JSCC) in SemComs is not compatible with the existing communication systems.<n>We propose an adaptive source-channel coding scheme for SemComs over parallel Gaussian channels.
  arXiv  Detail & Related papers  (2025-08-11T13:09:54Z)
• VQ-DeepISC: Vector Quantized-Enabled Digital Semantic Communication with Channel Adaptive Image Transmission [8.858565507331395]
  Discretization of semantic features enables interoperability between semantic and digital communication systems.<n>We propose a vector quantized (VQ)-enabled digital semantic communication system with channel adaptive image transmission.
  arXiv  Detail & Related papers  (2025-08-01T02:35:34Z)
• DNN-Based Precoding in RIS-Aided mmWave MIMO Systems With Practical Phase Shift [43.56429251312585]
  This paper investigates maximizing the throughput of millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems with obstructed direct communication paths.<n>A reconfigurable intelligent surface (RIS) is employed to enhance transmissions, considering mmWave characteristics related to line-of-sight (LoS) and multipath effects.<n>Deep neural network (DNN) is developed to facilitate faster codeword selection.
  arXiv  Detail & Related papers  (2025-07-03T17:35:06Z)
• Architectural Exploration of Hybrid Neural Decoders for Neuromorphic Implantable BMI [5.76010717601678]
  This work presents an efficient decoding pipeline for neuromorphic implantable brain-machine interfaces (Neu-iBMI)<n>We introduce a tunable event filter (EvFilter), which also functions as a spike detector (EvFilter-SPD), significantly reducing the number of events processed for decoding by 192X and 554X, respectively.
  arXiv  Detail & Related papers  (2025-05-09T12:15:09Z)
• Neuromorphic Wireless Split Computing with Multi-Level Spikes [69.73249913506042]
  Neuromorphic computing uses spiking neural networks (SNNs) to perform inference tasks.<n> embedding a small payload within each spike exchanged between spiking neurons can enhance inference accuracy without increasing energy consumption.<n> split computing - where an SNN is partitioned across two devices - is a promising solution.<n>This paper presents the first comprehensive study of a neuromorphic wireless split computing architecture that employs multi-level SNNs.
  arXiv  Detail & Related papers  (2024-11-07T14:08:35Z)
• Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers without Retraining [50.00291020618743]
  This work introduces a novel, parameter-adaptive AMPC architecture capable of online tuning without recomputing large datasets and retraining. We showcase the effectiveness of parameter-adaptive AMPC by controlling the swing-ups of two different real cartpole systems with a severely resource-constrained microcontroller (MCU) Taken together, these contributions represent a marked step toward the practical application of AMPC in real-world systems.
  arXiv  Detail & Related papers  (2024-04-08T20:02:19Z)
• SPAIC: A sub-$\mu$W/Channel, 16-Channel General-Purpose Event-Based Analog Front-End with Dual-Mode Encoders [6.6017549029623535]
  Low-power event-based analog front-ends are crucial to build efficient neuromorphic processing systems. We present a novel, highly analog front-end chip, denoted as SPAIC (signal-to-spike converter for analog AI computation) It offers a general-purpose dual-mode analog signal-to-spike encoding with delta modulation and pulse frequency modulation, with tunable frequency bands.
  arXiv  Detail & Related papers  (2023-08-31T19:53:04Z)
• A system design approach toward integrated cryogenic quantum control systems [0.3182204845791219]
  We provide a system level perspective on the design of control electronics for large scale quantum systems. Cryogenic CMOS plays a crucial role in the realization of scalable quantum computers.
  arXiv  Detail & Related papers  (2022-11-03T18:24:27Z)
• Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access [65.21117658030235]
  This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead. Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
  arXiv  Detail & Related papers  (2022-05-02T12:23:55Z)
• End-to-End Learning of Neuromorphic Wireless Systems for Low-Power Edge Artificial Intelligence [38.518936229794214]
  We introduce a novel "all-spike" low-power solution for remote wireless inference that is based on neuromorphic sensing, Impulse Radio (IR), and Spiking Neural Networks (SNNs) We introduce an end-to-end training procedure that treats the cascade of encoder, channel, and decoder as a probabilistic SNN-based autoencoder that implements Joint Source-Channel Coding (JSCC) The experiments confirm that the proposed end-to-end neuromorphic edge architecture provides a promising framework for efficient and low-latency remote sensing, communication, and inference.
  arXiv  Detail & Related papers  (2020-09-03T09:10:16Z)
• Massive MIMO As an Extreme Learning Machine [83.12538841141892]
  A massive multiple-input multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs) forms a natural extreme learning machine (ELM) By adding random biases to the received signals and optimizing the ELM output weights, the system can effectively tackle hardware impairments.
  arXiv  Detail & Related papers  (2020-07-01T04:15:20Z)

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.