Julien Su | Director, Product Development Div.
Macronix

Julien Su, Director, Product Development Div., Macronix

Julien Su is a Director in Product R&D Division at Macronix, specializing in hardware/software co-design at different areas in storage field specific to Flash devices. His expertise spans Flash design, SSD controller development, RAID sub-system and storage system applications. Julien has been devoted to storage sub-system implementation by leveraging novel heterogeneous SSDs. His current efforts integrate orchestration, cache, and tiered storage layers to support emerging AI workloads. Julien holds a Master's in Electronics Engineering from National Chiao Tung University, Taiwan, in 1996, where he focused on semiconductor physics, processing, and device characterization. His long-term vision is to build agile storage systems to meet demanding requirements in this booming age of AI.

Appearances:



Future of Memory and Storage - Day 1 @ 10:00

A Dual-tier SSD Controller Architecture for Latency-sensitive Workloads

Modern SSD controller architectures are increasingly shaped by the high demands of AI workloads, which create conflicting performance, capacity, and cost requirements. To address these competing goals, existing NVMe FDP controllers offer performance isolation but cannot provide QoS differentiation. Further, many SSDs leverage the capability of multi-bit Flash devices to support a high-speed SLC mode for internal tiering or caching. However, these capabilities remain invisible to the application layer and often result in substantial unpredictable performancefluctuations.This presentation introduces a novel dual-tier controller architecture ST-SSD designed for modern workloads. By enabling simultaneous user-defined capacity and performance tiers within a single drive, the DT-SSD simplifies device inventorymanagement and enables workload-specific configurations. Leveraging the NVMe FDP specification, the DT-SSD ensures predictable QoS through guaranteed bandwidth and IO prioritization. We analyze the design trade-offs, provide performance benchmarks, and evaluate how dynamic over-provisioning, wear balancing, and heat-tracking algorithms contribute to overall system efficiency.

last published: 19/May/26 18:25 GMT

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