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09/29/2022
Real-Time Scheduling of Machine Learning Operations on Heterogeneous Neuromorphic SoC
Neuromorphic Systems-on-Chip (NSoCs) are becoming heterogeneous by integ...
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06/27/2022
A Coupled Neural Circuit Design for Guillain-Barre Syndrome
Guillain-Barre syndrome is a rare neurological condition in which the hu...
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05/27/2022
Multiscale Voxel Based Decoding For Enhanced Natural Image Reconstruction From Brain Activity
Reconstructing perceived images from human brain activity monitored by f...
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05/26/2022
Learning in Feedback-driven Recurrent Spiking Neural Networks using full-FORCE Training
Feedback-driven recurrent spiking neural networks (RSNNs) are powerful c...
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04/06/2022
A Design Methodology for Fault-Tolerant Computing using Astrocyte Neural Networks
We propose a design methodology to facilitate fault tolerance of deep le...
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03/10/2022
Design-Technology Co-Optimization for NVM-based Neuromorphic Processing Elements
Neuromorphic hardware platforms can significantly lower the energy overh...
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02/21/2022
Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants
Precise monitoring of respiratory rate in premature infants is essential...
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02/17/2022
Implementing Spiking Neural Networks on Neuromorphic Architectures: A Review
Recently, both industry and academia have proposed several different neu...
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01/27/2022
On the Mitigation of Read Disturbances in Neuromorphic Inference Hardware
Non-Volatile Memory (NVM) cells are used in neuromorphic hardware to sto...
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11/23/2021
Design of Many-Core Big Little μBrain for Energy-Efficient Embedded Neuromorphic Computing
As spiking-based deep learning inference applications are increasing in ...
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10/15/2021
Design Technology Co-Optimization for Neuromorphic Computing
We present a design-technology tradeoff analysis in implementing machine...
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08/27/2021
A Design Flow for Mapping Spiking Neural Networks to Many-Core Neuromorphic Hardware
The design of many-core neuromorphic hardware is getting more and more c...
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08/04/2021
DFSynthesizer: Dataflow-based Synthesis of Spiking Neural Networks to Neuromorphic Hardware
Spiking Neural Networks (SNN) are an emerging computation model, which u...
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06/16/2021
Improving Inference Lifetime of Neuromorphic Systems via Intelligent Synapse Mapping
Non-Volatile Memories (NVMs) such as Resistive RAM (RRAM) are used in ne...
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05/05/2021
Dynamic Reliability Management in Neuromorphic Computing
Neuromorphic computing systems uses non-volatile memory (NVM) to impleme...
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05/04/2021
NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks
Recently, both industry and academia have proposed many different neurom...
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03/22/2021
On the Role of System Software in Energy Management of Neuromorphic Computing
Neuromorphic computing systems such as DYNAPs and Loihi have recently be...
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03/22/2021
Special Session: Reliability Analysis for ML/AI Hardware
Artificial intelligence (AI) and Machine Learning (ML) are becoming perv...
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03/09/2021
Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware
Neuromorphic computing systems are embracing memristors to implement hig...
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01/21/2021
Motif Identification using CNN-based Pairwise Subsequence Alignment Score Prediction
A common problem in bioinformatics is related to identifying gene regula...
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11/30/2020
Aging-Aware Request Scheduling for Non-Volatile Main Memory
Modern computing systems are embracing non-volatile memory (NVM) to impl...
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11/27/2020
Compiling Spiking Neural Networks to Mitigate Neuromorphic Hardware Constraints
Spiking Neural Networks (SNNs) are efficient computation models to perfo...
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11/27/2020
Design Methodologies for Reliable and Energy-efficient PCM Systems
Phase-change memory (PCM) is a scalable and low latency non-volatile mem...
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11/07/2020
Machine learning applications to DNA subsequence and restriction site analysis
Based on the BioBricks standard, restriction synthesis is a novel catabo...
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10/09/2020
Thermal-Aware Compilation of Spiking Neural Networks to Neuromorphic Hardware
Hardware implementation of neuromorphic computing can significantly impr...
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09/26/2020
Reliability-Performance Trade-offs in Neuromorphic Computing
Neuromorphic architectures built with Non-Volatile Memory (NVM) can sign...
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09/19/2020
Enabling Resource-Aware Mapping of Spiking Neural Networks via Spatial Decomposition
With growing model complexity, mapping Spiking Neural Network (SNN)-base...
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07/04/2020
A Case for Lifetime Reliability-Aware Neuromorphic Computing
Neuromorphic computing with non-volatile memory (NVM) can significantly ...
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06/11/2020
Run-time Mapping of Spiking Neural Networks to Neuromorphic Hardware
In this paper, we propose a design methodology to partition and map the ...
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06/10/2020
Improving Dependability of Neuromorphic Computing With Non-Volatile Memory
As process technology continues to scale aggressively, circuit aging in ...
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05/10/2020
Improving Phase Change Memory Performance with Data Content Aware Access
A prominent characteristic of write operation in Phase-Change Memory (PC...
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05/10/2020
Exploiting Inter- and Intra-Memory Asymmetries for Data Mapping in Hybrid Tiered-Memories
Modern computing systems are embracing hybrid memory comprising of DRAM ...
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04/07/2020
Compiling Spiking Neural Networks to Neuromorphic Hardware
Machine learning applications that are implemented with spike-based comp...
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03/21/2020
PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network
We present PyCARL, a PyNN-based common Python programming interface for ...
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11/01/2019
A Framework to Explore Workload-Specific Performance and Lifetime Trade-offs in Neuromorphic Computing
Neuromorphic hardware with non-volatile memory (NVM) can implement machi...
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09/04/2019
Mapping Spiking Neural Networks to Neuromorphic Hardware
Neuromorphic hardware platforms implement biological neurons and synapse...
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08/21/2019
Enabling and Exploiting Partition-Level Parallelism (PALP) in Phase Change Memories
Phase-change memory (PCM) devices have multiple banks to serve memory re...
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08/13/2019
Mapping of Local and Global Synapses on Spiking Neuromorphic Hardware
Spiking Neural Networks (SNNs) are widely deployed to solve complex patt...
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08/13/2019
Heartbeat Classification in Wearables Using Multi-layer Perceptron and Time-Frequency Joint Distribution of ECG
Heartbeat classification using electrocardiogram (ECG) data is a vital a...
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05/30/2018
Reference-free Calibration in Sensor Networks
Sensor calibration is one of the fundamental challenges in large-scale I...
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07/18/2017
