89 citations · 314 across the 33 of their papers we have counts for
14 papers · 1 filter
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}.…
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…
PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network
Adarsha Balaji, Prathyusha Adiraju, Hirak J. Kashyap +4
We present PyCARL, a PyNN-based common Python programming interface for hardware-software co-simulation of spiking neural network (SNN). Through PyCARL, we make the following two k…