4 papers · 1 filter
BLINK: Batch Normalization-based Integrity Checkpoints for In-Situ Detection and Mitigation of Diverse Weight Corruptions in DNN Accelerators
Marzia Khan, Akul Malhotra, Sumeet Kumar Gupta
In safety-critical deployments, AI hardware must remain reliable against a broad spectrum of threats such as aging, soft errors, hard faults, and adversarial attacks (e.g. progress…
Weight Transformations in Bit-Sliced Crossbar Arrays for Fault Tolerant Computing-in-Memory: Design Techniques and Evaluation Framework
Akul Malhotra, Sumeet Kumar Gupta
The deployment of deep neural networks (DNNs) on compute-in-memory (CiM) accelerators offers significant energy savings and speed-up by reducing data movement during inference. How…
BinSparX: Sparsified Binary Neural Networks for Reduced Hardware Non-Idealities in Xbar Arrays
Akul Malhotra, Sumeet Kumar Gupta
Compute-in-memory (CiM)-based binary neural network (CiM-BNN) accelerators marry the benefits of CiM and ultra-low precision quantization, making them highly suitable for edge comp…
SiTe CiM: Signed Ternary Computing-in-Memory for Ultra-Low Precision Deep Neural Networks
Niharika Thakuria, Akul Malhotra, Sandeep K. Thirumala +3
Ternary Deep Neural Networks (DNN) have shown a large potential for highly energy-constrained systems by virtue of their low power operation (due to ultra-low precision) with only…