Junhwan Kim

HyunJae Lee, Gihyeon Lee, Junhwan Kim et al.

Despite the evolution of Convolutional Neural Networks (CNNs), their performance is surprisingly dependent on the choice of hyperparameters. However, it remains challenging to efficiently explore large hyperparameter search space due to the long training times of modern CNNs. Multi-fidelity optimization enables the exploration of more hyperparameter configurations given budget by early termination of unpromising configurations. However, it often results in selecting a sub-optimal configuration as training with the high-performing configuration typically converges slowly in an early phase. In this paper, we propose Multi-fidelity Optimization with a Recurring Learning rate (MORL) which incorporates CNNs' optimization process into multi-fidelity optimization. MORL alleviates the problem of slow-starter and achieves a more precise low-fidelity approximation. Our comprehensive experiments on general image classification, transfer learning, and semi-supervised learning demonstrate the effectiveness of MORL over other multi-fidelity optimization methods such as Successive Halving Algorithm (SHA) and Hyperband. Furthermore, it achieves significant performance improvements over hand-tuned hyperparameter configuration within a practical budget.

Junhwan Kim, Seunghyun Kim, Yesin Ryu et al.

As DRAM scales to higher density and I/O speeds, ensuring data correctness becomes increasingly difficult. Industry has responded with a three-layer stack: on-die ECC (O-ECC), link ECC (L-ECC), and system ECC (S-ECC). However, these layers have evolved independently, often duplicating redundancy, leaving coverage gaps, and occasionally interfering. We propose Cerberus, a cross-layer ECC co-design that unifies protection across device, link, and system while preserving the native role of each layer. At its core is an Encode-Once, Decode-Many (EODM) architecture: the controller performs a single encoding whose redundancy is reused by L-ECC for immediate write-path detection and retry, by O-ECC for in-device repair on reads, and by S-ECC for strong end-to-end recovery. Cerberus jointly designs complementary parity and syndrome structures, orders decoders, and allocates the correction budget to prevent miscorrection amplification and enable selective correction under tight redundancy constraints. Our evaluations show improved resilience to clustered and peripheral faults while reducing redundant overhead, underscoring the importance of coordinated cross-layer protection for next-generation memory systems, such as custom HBMs.