Reliable and Energy-efficient Diabetic Retinopathy Screening using Memristor-based Neural Networks

Diabetic retinopathy (DR) is a leading cause of permanent vision loss worldwide. It refers to irreversible retinal damage caused due to elevated glucose levels and blood pressure. Regular screening for DR can facilitate its early detection and timely treatment. Neural network-based DR classifiers can be leveraged to achieve such screening in...

Severity-based Hierarchical ECG Classification using Neural Networks

Timely detection of cardiac arrhythmia characterized by abnormal heartbeats can help in the early diagnosis and treatment of cardiovascular diseases. Wearable healthcare devices typically use neural networks to provide the most convenient way of continuously monitoring heart activity for arrhythmia detection. However, it is challenging to...

Efficient Signed Arithmetic Multiplication on Memristor-based Crossbar

The vast potential of memristor-based computation-in-memory (CIM) engines has mainly triggered the mapping of best-suited applications. Nevertheless, with additional support, existing applications can also benefit from CIM. In particular, this paper proposes an energy and area-efficient CIM-based methodology to perform arithmetic signed...

Testing RRAM and Computation-in-Memory Devices: Defects, Fault Models, and Test Solutions

Resistive random access memory (RRAM) is a promising emerging memory technology that offers dense, non-volatile memories that do not consume any static power. Furthermore, RRAMdevices can be written and read out in nanoseconds, and it is possible to use them to performcomputation-in-memory (CIM). These benefits make this technology a potential...

CIM-based Robust Logic Accelerator using 28 nm STT-MRAM Characterization Chip Tape-out

Spin-transfer torque magnetic random access memory (STT-MRAM) based computation-in-memory (CIM) architectures have shown great prospects for an energy-efficient computing. However, device variations and non-idealities narrow down the sensing margin that severely impacts the computing accuracy. In this work, we propose an adaptive referencing...