Seongyeon Park

Seongyeon Park, Myungseo Song, Bohyung Kim et al.

Neural text-to-speech (TTS) models can synthesize natural human speech when trained on large amounts of transcribed speech. However, collecting such large-scale transcribed data is expensive. This paper proposes an unsupervised pre-training method for a sequence-to-sequence TTS model by leveraging large untranscribed speech data. With our pre-training, we can remarkably reduce the amount of paired transcribed data required to train the model for the target downstream TTS task. The main idea is to pre-train the model to reconstruct de-warped mel-spectrograms from warped ones, which may allow the model to learn proper temporal assignment relation between input and output sequences. In addition, we propose a data augmentation method that further improves the data efficiency in fine-tuning. We empirically demonstrate the effectiveness of our proposed method in low-resource language scenarios, achieving outstanding performance compared to competing methods. The code and audio samples are available at: https://github.com/cnaigithub/SpeechDewarping

Junguk Hong, Changmin Shin, Sukjin Kim et al.

Lookup tables (LUTs) have recently gained attention as an alternative compute mechanism that maps input operands to precomputed results, eliminating the need for arithmetic logic. LUTs not only reduce logic complexity, but also naturally support diverse numerical precisions without requiring separate circuits for each bitwidth-an increasingly important feature in quantized DNNs. This creates a favorable tradeoff in PIM: memory capacity can be used in place of logic to increase computational throughput, aligning well with DRAM-PIM architectures that offer high bandwidth and easily available memory but limited logic density. In this work, we explore this capacity-computation tradeoff in LUT-based PIM designs, where memory capacity is traded for performance by packing multiple MAC operations into a single LUT lookup. Building on this insight, we propose LOCALUT, a PIM-based design for efficient low-bit quantized DNN inference using operation-packed LUTs. First, we observe that these LUTs contain extensive redundancy and introduce LUT canonicalization, which eliminates duplicate entries to reduce LUT size. Second, we propose reordering LUT, a lightweight auxiliary LUT that remaps weight vectors to their canonical form required by LUT canonicalization with a simple LUT lookup. Third, we propose LUT slice streaming, a novel execution strategy that exploits the DRAM-buffer hierarchy by streaming only relevant LUT columns into the buffer and reusing them across multiple weight vectors. Evaluated on a real system based on UPMEM devices, we demonstrate a geometric mean speedup of 1.82x across various numeric precisions and DNN models. We believe LOCALUT opens a path toward scalable, low-logic PIM designs tailored for LUT-based DNN inference. Our implementation of LOCALUT is available at https://github.com/AIS-SNU/LoCaLUT.

Jaeyong Song, Hongsun Jang, Changmin Shin et al.

On-disk graph-based vector search (GVS) has become the dominant approach for serving large-scale vector databases at high recall, but prior systems struggle to sustain concurrent search and update throughput on high-dimensional workloads. We find the main cause of this in position seeking, a full graph traversal that every update performs to locate neighbors before linking the new vector into the graph. Position seeking is fundamentally heavier than a search query, and its cost is further amplified by two systemic limitations of current GVS systems, packed layouts that couple every edge fetch to a full vector load, and a static entrance graph whose entry points drift away from newly inserted regions as updates accumulate. We present NAVIS, an on-SSD GVS system that drives down position-seeking overhead through (i) a layout-supported selective vector read that breaks the packed-page coupling without losing its locality benefits, (ii) a dynamic lightweight entrance graph update mechanism that reuses traversal information already produced by concurrent updates, and (iii) an entrance graph-aware edgelist cache that concentrates capacity on high-reuse paths near refreshed entry points. Across multiple large-scale high-dimensional benchmarks, NAVIS enhances average insertion throughput by up to 2.74x and average concurrent search throughput by up to 1.37x while reducing average search latency by up to 25.26%.

Jaeyong Song, Seongyeon Park, Hongsun Jang et al.

Full-graph training of graph neural networks (GNNs) is widely used as it enables direct validation of algorithmic improvements by preserving complete neighborhood information. However, it typically requires multiple GPUs or servers, incurring substantial hardware and inter-device communication costs. While existing single-server methods reduce infrastructure requirements, they remain constrained by GPU and host memory capacity as graph sizes increase. To address this limitation, we introduce GriNNder, which is the first work to leverage storage devices to enable full-graph training even with limited memory. Because modern NVMe SSDs offer multi-terabyte capacities and bandwidths exceeding 10 GB/s, they provide an appealing option when memory resources are scarce. Yet, directly applying storage-based methods from other domains fails to address the unique access patterns and data dependencies in full-graph GNN training. GriNNder tackles these challenges by structured storage offloading (SSO), a framework that manages the GPU-host-storage hierarchy through coordinated cache, (re)gather, and bypass mechanisms. To realize the framework, we devise (i) a partition-wise caching strategy for host memory that exploits the observation on cross-partition dependencies, (ii) a regathering strategy for gradient computation that eliminates redundant storage operations, and (iii) a lightweight partitioning scheme that mitigates the memory requirements of existing graph partitioners. In experiments performed over various models and datasets, GriNNder achieves up to 9.78x speedup over state-of-the-art baselines and throughput comparable to distributed systems, enabling previously infeasible large-scale full-graph training even on a single GPU.

Seongyeon Park, Bohyung Kim, Tae-hyun Oh

Recently, zero-shot TTS and VC methods have gained attention due to their practicality of being able to generate voices even unseen during training. Among these methods, zero-shot modifications of the VITS model have shown superior performance, while having useful properties inherited from VITS. However, the performance of VITS and VITS-based zero-shot models vary dramatically depending on how the losses are balanced. This can be problematic, as it requires a burdensome procedure of tuning loss balance hyper-parameters to find the optimal balance. In this work, we propose a novel framework that finds this optimum without search, by inducing the decoder of VITS-based models to its full reconstruction ability. With our framework, we show superior performance compared to baselines in zero-shot TTS and VC, achieving state-of-the-art performance. Furthermore, we show the robustness of our framework in various settings. We provide an explanation for the results in the discussion.