At this time, Prof. Kim and other advised students have studied binarized neural networks (BNNs) from this summer. For 1-D data (e.g. sound), BNNs are a very realistic form to be implemented on lightweight MCUs. Besides, their low latency and low power consumption can extend the applicable area of neural networks.
On June 26, 2022, a manuscript titled as "CTMQ: Cyclic Training of Convolutional Neural Networks with Multiple Quantization Steps" has been uploaded in arXiv.
On May 23/24, 2022, there is an open lab ceremony to explain the life of graduate schools and method for achieving research contributions. (#403, 2nd Engineering building, Dankook University)
Prof. HyunJin Kim had a meeting with ARM to apply the ARM Academic Access (AAA) Program. The program supports ARM commercial IPs, training Program, and Materials for researches with ARM IPs.
A paper titled "A storage-efficient ensemble classification using filter sharing on binarized convolutional neural networks" by HyunJin Kim, Mohammed Alnemari, and Nader Bagherzadeh has been accepted in PeerJ Computer Science.
A paper titled "PresB-Net: parametric binarized neural network with learnable activations and shuffled grouped convolution" by Jungwoo Shin and HyunJin Kim has been accepted in PeerJ Computer Science.
A paper titled "Highly accurate approximate multiplier using heterogeneous inexact 4-2 compressors for error-resilient application" by Jaewoo Lee and HyunJin Kim has been accepted in IEMEK Journal of embedded Systems and Applications (Domestic).
A paper titled "A Cost-Efficient Approximate Dynamic Ranged Multiplication and Approximation-Aware Training on Convolutional Neural Networks" by HyunJin Kim and Alberto A. Del Barrio has been accepted in IEEE Access.
A paper titled "PLAM: a Posit Logarithm-Approximate Multiplier" by Raul Murillo, Alberto A. Del Barrio, Guillermo Botella, Min Soo Kim, HyunJin Kim, Nader Bagherzadeh has been accepted in IEEE Transactions on Emerging Topics in Computing.
A project titled "ACDNN: Approximate Computing-based Deep Neural Networks using Inaccurate Arithmetic Units for Low-Power Systems" will be supported by National Research Foundation (NRF) (June 2021 ~ February 2024) - 2021.05.27.
A paper titled “A k-Mismatch String Matching for Generalized Edit Distance using Diagonal Skipping Method” by HyunJin Kim has been accepted in PLOS One.
A paper titled "AresB-Net: accurate residual binarized neural networks using shortcut concatenation and shuffled grouped convolution" by HyunJin Kim has been accepted in PeerJ Computer Science.
A paper titled "A Low-Cost Compensated Approximate Multiplier for Bfloat16 Data Processing on CNN Inference" by HyunJin Kim has been accepted in ETRI Journal.
A Paper titled "Effects of Approximate Multiplication on Convolutional Neural Networks," Kim, M. S., Del Barrio, A. A., Kim, H., & Bagherzadeh, N has been published in the early access of IEEE Transactions on Emerging Topics in Computing.