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Found 10 papers, 3 papers with code
Realtime Person Identification via Gait Analysis
In this paper, we propose a small CNN model with 4 layers that is very amenable for edge AI deployment and realtime gait recognition.
Towards a Design Framework for TNN-Based Neuromorphic Sensory Processing Units
Temporal Neural Networks (TNNs) are spiking neural networks that exhibit brain-like sensory processing with high energy efficiency.
TNN7: A Custom Macro Suite for Implementing Highly Optimized Designs of Neuromorphic TNNs
Recent works have proposed a microarchitecture framework for implementing TNNs and demonstrated competitive performance on vision and time-series applications.
A Microarchitecture Implementation Framework for Online Learning with Temporal Neural Networks
Temporal Neural Networks (TNNs) are spiking neural networks that use time as a resource to represent and process information, similar to the mammalian neocortex.
Unsupervised Clustering of Time Series Signals using Neuromorphic Energy-Efficient Temporal Neural Networks
Unsupervised time series clustering is a challenging problem with diverse industrial applications such as anomaly detection, bio-wearables, etc.
A Custom 7nm CMOS Standard Cell Library for Implementing TNN-based Neuromorphic Processors
A set of highly-optimized custom macro extensions is developed for a 7nm CMOS cell library for implementing Temporal Neural Networks (TNNs) that can mimic brain-like sensory processing with extreme energy efficiency.
Direct CMOS Implementation of Neuromorphic Temporal Neural Networks for Sensory Processing
The TNN microarchitecture framework is embodied in a set of characteristic equations for assessing the total gate count, die area, compute time, and power consumption for any TNN design.
Using GANs for Generation of Realistic City-Scale Ride Sharing/Hailing Data Sets
This paper focuses on the synthetic generation of human mobility data in urban areas.
On the Real-time Vehicle Placement Problem
Motivated by ride-sharing platforms' efforts to reduce their riders' wait times for a vehicle, this paper introduces a novel problem of placing vehicles to fulfill real-time pickup requests in a spatially and temporally changing environment.
Space-Time Graph Modeling of Ride Requests Based on Real-World Data
We show the pattern of ride requests and the potential of ride pooling for a city can be characterized by the densification factor of the ride request graphs.
