A Trainable Sequence Learner that Learns and Recognizes Two-Input Sequence Patterns

10/21/2022
by   Jan Hohenheim, et al.
0

We present two designs for an analog circuit that can learn to detect a temporal sequence of two inputs. The training phase is done by feeding the circuit with the desired sequence and, after the training is completed, each time the trained sequence is encountered again the circuit will emit a signal of correct recognition. Sequences are in the order of tens of nanoseconds. The first design can reset the trained sequence on runtime but assumes very strict timing of the inputs. The second design can only be trained once but is lenient in the input's timing.

READ FULL TEXT
research
08/08/2023

Implementation Of MNIST Dataset Learning Using Analog Circuit

There have been many attempts to implement neural networks in the analog...
research
05/14/2017

Timing Model Extraction for Sequential Circuits Considering Process Variations

As semiconductor devices continue to scale down, process vari- ations be...
research
05/14/2017

On Timing Model Extraction and Hierarchical Statistical Timing Analysis

In this paper, we investigate the challenges to apply Statistical Static...
research
06/20/2016

Polymetric Rhythmic Feel for a Cognitive Drum Computer

This paper addresses a question about music cognition: how do we derive ...
research
07/13/2022

RobustAnalog: Fast Variation-Aware Analog Circuit Design Via Multi-task RL

Analog/mixed-signal circuit design is one of the most complex and time-c...
research
06/30/2019

Towards Wireless Health Monitoring via Analog Signal Compression based Biosensing Platform

Wireless all-analog biosensor design for concurrent microfluidic and phy...
research
04/06/2020

Hardware Trojan Detection Using Controlled Circuit Aging

This paper reports a novel approach that uses transistor aging in an int...

Please sign up or login with your details

Forgot password? Click here to reset