89 citations · 314 across the 33 of their papers we have counts for
36 papers
Real-Time Scheduling of Machine Learning Operations on Heterogeneous Neuromorphic SoC
Anup Das
Neuromorphic Systems-on-Chip (NSoCs) are becoming heterogeneous by integrating general-purpose processors (GPPs) and neural processing units (NPUs) on the same SoC. For embedded sy…
A Design Methodology for Fault-Tolerant Computing using Astrocyte Neural Networks
Murat Işık, Ankita Paul, M. Lakshmi Varshika +1
We propose a design methodology to facilitate fault tolerance of deep learning models. First, we implement a many-core fault-tolerant neuromorphic hardware design, where neuron and…
Design-Technology Co-Optimization for NVM-based Neuromorphic Processing Elements
Shihao Song, Adarsha Balaji, Anup Das +1
Neuromorphic hardware platforms can significantly lower the energy overhead of a machine learning inference task. We present a design-technology tradeoff analysis to implement such…
Implementing Spiking Neural Networks on Neuromorphic Architectures: A Review
Phu Khanh Huynh, M. Lakshmi Varshika, Ankita Paul +3
Recently, both industry and academia have proposed several different neuromorphic systems to execute machine learning applications that are designed using Spiking Neural Networks (…
On the Mitigation of Read Disturbances in Neuromorphic Inference Hardware
Ankita Paul, Shihao Song, Twisha Titirsha +1
Non-Volatile Memory (NVM) cells are used in neuromorphic hardware to store model parameters, which are programmed as resistance states. NVMs suffer from the read disturb issue, whe…
Design of Many-Core Big Little μBrain for Energy-Efficient Embedded Neuromorphic Computing
M. Lakshmi Varshika, Adarsha Balaji, Federico Corradi +3
As spiking-based deep learning inference applications are increasing in embedded systems, these systems tend to integrate neuromorphic accelerators such as Brain to improve ener…
Design Technology Co-Optimization for Neuromorphic Computing
Ankita Paul, Shihao Song, Anup Das
We present a design-technology tradeoff analysis in implementing machine-learning inference on the processing cores of a Non-Volatile Memory (NVM)-based many-core neuromorphic hard…
A Design Flow for Mapping Spiking Neural Networks to Many-Core Neuromorphic Hardware
Shihao Song, M. Lakshmi Varshika, Anup Das +1
The design of many-core neuromorphic hardware is getting more and more complex as these systems are expected to execute large machine learning models. To deal with the design compl…
DFSynthesizer: Dataflow-based Synthesis of Spiking Neural Networks to Neuromorphic Hardware
Shihao Song, Harry Chong, Adarsha Balaji +3
Spiking Neural Networks (SNN) are an emerging computation model, which uses event-driven activation and bio-inspired learning algorithms. SNN-based machine-learning programs are ty…
Improving Inference Lifetime of Neuromorphic Systems via Intelligent Synapse Mapping
Shihao Song, Twisha Titirsha, Anup Das
Non-Volatile Memories (NVMs) such as Resistive RAM (RRAM) are used in neuromorphic systems to implement high-density and low-power analog synaptic weights. Unfortunately, an RRAM c…
Dynamic Reliability Management in Neuromorphic Computing
Shihao Song, Jui Hanamshet, Adarsha Balaji +5
Neuromorphic computing systems uses non-volatile memory (NVM) to implement high-density and low-energy synaptic storage. Elevated voltages and currents needed to operate NVMs cause…
NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks
Adarsha Balaji, Shihao Song, Twisha Titirsha +6
Recently, both industry and academia have proposed many different neuromorphic architectures to execute applications that are designed with Spiking Neural Network (SNN). Consequent…
Special Session: Reliability Analysis for ML/AI Hardware
Shamik Kundu, Kanad Basu, Mehdi Sadi +4
Artificial intelligence (AI) and Machine Learning (ML) are becoming pervasive in today's applications, such as autonomous vehicles, healthcare, aerospace, cybersecurity, and many c…
On the Role of System Software in Energy Management of Neuromorphic Computing
Twisha Titirsha, Shihao Song, Adarsha Balaji +1
Neuromorphic computing systems such as DYNAPs and Loihi have recently been introduced to the computing community to improve performance and energy efficiency of machine learning pr…
Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware
Twisha Titirsha, Shihao Song, Anup Das +4
Neuromorphic computing systems are embracing memristors to implement high density and low power synaptic storage as crossbar arrays in hardware. These systems are energy efficient…
Machine learning applications to DNA subsequence and restriction site analysis
Ethan J. Moyer, Anup Das
Based on the BioBricks standard, restriction synthesis is a novel catabolic iterative DNA synthesis method that utilizes endonucleases to synthesize a query sequence from a referen…
Aging-Aware Request Scheduling for Non-Volatile Main Memory
Shihao Song, Anup Das, Onur Mutlu +1
Modern computing systems are embracing non-volatile memory (NVM) to implement high-capacity and low-cost main memory. Elevated operating voltages of NVM accelerate the aging of CMO…
Compiling Spiking Neural Networks to Mitigate Neuromorphic Hardware Constraints
Adarsha Balaji, Anup Das
Spiking Neural Networks (SNNs) are efficient computation models to perform spatio-temporal pattern recognition on {resource}- and {power}-constrained platforms. SNNs executed on ne…
Design Methodologies for Reliable and Energy-efficient PCM Systems
Shihao Song, Anup Das
Phase-change memory (PCM) is a scalable and low latency non-volatile memory (NVM) technology that has been proposed to serve as storage class memory (SCM), providing low access lat…
Thermal-Aware Compilation of Spiking Neural Networks to Neuromorphic Hardware
Twisha Titirsha, Anup Das
Hardware implementation of neuromorphic computing can significantly improve performance and energy efficiency of machine learning tasks implemented with spiking neural networks (SN…
Reliability-Performance Trade-offs in Neuromorphic Computing
Twisha Titirsha, Anup Das
Neuromorphic architectures built with Non-Volatile Memory (NVM) can significantly improve the energy efficiency of machine learning tasks designed with Spiking Neural Networks (SNN…
Enabling Resource-Aware Mapping of Spiking Neural Networks via Spatial Decomposition
Adarsha Balaji, Shihao Song, Anup Das +5
With growing model complexity, mapping Spiking Neural Network (SNN)-based applications to tile-based neuromorphic hardware is becoming increasingly challenging. This is because the…
A Case for Lifetime Reliability-Aware Neuromorphic Computing
Shihao Song, Anup Das
Neuromorphic computing with non-volatile memory (NVM) can significantly improve performance and lower energy consumption of machine learning tasks implemented using spike-based com…
Run-time Mapping of Spiking Neural Networks to Neuromorphic Hardware
Adarsha Balaji, Thibaut Marty, Anup Das +1
In this paper, we propose a design methodology to partition and map the neurons and synapses of online learning SNN-based applications to neuromorphic architectures at {run-time}.…