Handwritten Digit Recognition
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Benchmarks
Most implemented
LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence
Integrated Gradient Correlation: a Dataset-wise Attribution Method
MNIST-MIX: A Multi-language Handwritten Digit Recognition Dataset
Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML
How Important is Weight Symmetry in Backpropagation?
Papers
myMNIST: Benchmark of PETNN, KAN, and Classical Deep Learning Models for Burmese Handwritten Digit Recognition
We present the first systematic benchmark on a standardized iteration of the publicly available Burmese Handwritten Digit Dataset (BHDD), which we have designated as myMNIST Benchmarking. While BHDD serves as a foundatio…
Handwritten Digit RecognitionAn Event-Driven E-Skin System with Dynamic Binary Scanning and real time SNN Classification
This paper presents a novel hardware system for high-speed, event-sparse sampling-based electronic skin (e-skin)that integrates sensing and neuromorphic computing. The system is built around a 16x16 piezoresistive tactil…
Handwritten Digit RecognitionReward-Modulated Local Learning in Spiking Encoders: Controlled Benchmarks with STDP and Hybrid Rate Readouts
This paper presents a controlled empirical study of biologically motivated local learning for handwritten digit recognition. We evaluate an STDP-inspired competitive proxy and a practical hybrid benchmark built on the sa…
Handwritten Digit RecognitionRAMAN: Resource-efficient ApproxiMate Posit Processing for Algorithm-Hardware Co-desigN
Edge-AI applications still face considerable challenges in enhancing computational efficiency in resource-constrained environments. This work presents RAMAN, a resource-efficient and approximate posit(8,2)-based Multiply…
Handwritten Digit RecognitionComputational EfficiencyApplication of Machine Learning for Correcting Defect-induced Neuromorphic Circuit Inference Errors
This paper presents a machine learning-based approach to correct inference errors caused by stuck-at faults in fully analog ReRAM-based neuromorphic circuits. Using a Design-Technology Co-Optimization (DTCO) simulation f…
Handwritten Digit RecognitionLow Power Approximate Multiplier Architecture for Deep Neural Networks
This paper proposes an low power approximate multiplier architecture for deep neural network (DNN) applications. A 4:2 compressor, introducing only a single combination error, is designed and integrated into an 8x8 unsig…
Handwritten Digit RecognitionImage Denoising