3 citations · 5 across the 6 of their papers we have counts for
6 papers
MCEL: Margin-Based Cross-Entropy Loss for Error-Tolerant Quantized Neural Networks
Mikail Yayla, Akash Kumar
Robustness to bit errors is a key requirement for the reliable use of neural networks (NNs) on emerging approximate computing platforms and error-prone memory technologies. A commo…
HW/SW Codesign for Robust and Efficient Binarized SNNs by Capacitor Minimization
Mikail Yayla, Simon Thomann, Ming-Liang Wei +3
Using accelerators based on analog computing is an efficient way to process the immensely large workloads in Neural Networks (NNs). One example of an analog computing scheme for NN…
HEP-BNN: A Framework for Finding Low-Latency Execution Configurations of BNNs on Heterogeneous Multiprocessor Platforms
Leonard David Bereholschi, Ching-Chi Lin, Mikail Yayla +1
Binarized Neural Networks (BNNs) significantly reduce the computation and memory demands with binarized weights and activations compared to full-precision NNs. Executing a layer in…
Universal Approximation Theorems of Fully Connected Binarized Neural Networks
Mikail Yayla, Mario Günzel, Burim Ramosaj +1
Neural networks (NNs) are known for their high predictive accuracy in complex learning problems. Beside practical advantages, NNs also indicate favourable theoretical properties su…
Bit Error Tolerance Metrics for Binarized Neural Networks
Sebastian Buschjäger, Jian-Jia Chen, Kuan-Hsun Chen +5
To reduce the resource demand of neural network (NN) inference systems, it has been proposed to use approximate memory, in which the supply voltage and the timing parameters are tu…
Towards Explainable Bit Error Tolerance of Resistive RAM-Based Binarized Neural Networks
Sebastian Buschjäger, Jian-Jia Chen, Kuan-Hsun Chen +6
Non-volatile memory, such as resistive RAM (RRAM), is an emerging energy-efficient storage, especially for low-power machine learning models on the edge. It is reported, however, t…