47 citations · 133 across the 21 of their papers we have counts for
21 papers
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…
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 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…
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…
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…
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…
Improving Dependability of Neuromorphic Computing With Non-Volatile Memory
Shihao Song, Anup Das, Nagarajan Kandasamy
As process technology continues to scale aggressively, circuit aging in a neuromorphic hardware due to negative bias temperature instability (NBTI) and time-dependent dielectric br…
Compiling Spiking Neural Networks to Neuromorphic Hardware
Shihao Song, Adarsha Balaji, Anup Das +2
Machine learning applications that are implemented with spike-based computation model, e.g., Spiking Neural Network (SNN), have a great potential to lower the energy consumption wh…
Improving Phase Change Memory Performance with Data Content Aware Access
Shihao Song, Anup Das, Onur Mutlu +1
A prominent characteristic of write operation in Phase-Change Memory (PCM) is that its latency and energy are sensitive to the data to be written as well as the content that is ove…
Exploiting Inter- and Intra-Memory Asymmetries for Data Mapping in Hybrid Tiered-Memories
Shihao Song, Anup Das, Nagarajan Kandasamy
Modern computing systems are embracing hybrid memory comprising of DRAM and non-volatile memory (NVM) to combine the best properties of both memory technologies, achieving low late…
A Framework to Explore Workload-Specific Performance and Lifetime Trade-offs in Neuromorphic Computing
Adarsha Balaji, Shihao Song, Anup Das +4
Neuromorphic hardware with non-volatile memory (NVM) can implement machine learning workload in an energy-efficient manner. Unfortunately, certain NVMs such as phase change memory…
Enabling and Exploiting Partition-Level Parallelism (PALP) in Phase Change Memories
Shihao Song, Anup Das, Onur Mutlu +1
Phase-change memory (PCM) devices have multiple banks to serve memory requests in parallel. Unfortunately, if two requests go to the same bank, they have to be served one after ano…