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Related Experiment Video

Updated: May 3, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

SPIRE: A 28nm Memory-Efficient Multi-Reservoir LSM Accelerator for Adaptive and Flexible Time-series Classification.

Dario Fernandez-Khatiboun, Simon Richter, Yasser Rezaeiyan

IEEE Transactions on Biomedical Circuits and Systems

|February 25, 2026

Summary

This summary is machine-generated.

This study introduces SPIRE, a novel digital multi-reservoir liquid state machine (LSM) for efficient time-series classification. SPIRE significantly enhances synaptic density and reduces memory footprint for edge computing applications.

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A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

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P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

Area of Science:

Neuromorphic Engineering
Artificial Intelligence

Background:

Spiking Neural Networks (SNNs) excel at sequential data but struggle with long-term dependencies.
Liquid State Machines (LSMs) offer a solution by separating recurrence and classification, but hardware implementations face memory and performance trade-offs.

Purpose of the Study:

To develop a compact, memory-efficient, and high-performance multi-reservoir LSM hardware.
To address the design limitations of existing LSM implementations for time-series classification and edge deployment.

Main Methods:

Introduced SPIRE, a fully digital multi-reservoir LSM with online learning adaptation in TSMC 28nm CMOS.
Implemented a parallelized architecture with up to eight reservoir ensembles across four cores.
Utilized on-the-fly weight generation for reduced memory footprint and supported dual operation modes.

Main Results:

Achieved a synaptic density improvement of up to 18.46× compared to prior works.
Demonstrated high computational efficiency: 3.56 GSOPs/mm² (4.91 pJ/SOP) in sequential mode and 76.05 GSOPs/mm² (0.1 pJ/SOP) in parallel mode.
Operated at 55 MHz and 0.55 V, showcasing power and speed advantages.

Conclusions:

SPIRE offers a memory-efficient and high-performance solution for neuromorphic computing tasks like time-series classification.
The design advancements enable practical edge deployment of complex LSM models.
SPIRE represents a significant step forward in overcoming hardware limitations for brain-inspired computing.