Near-memory Dequantization Architecture In Custom HBM for LLM inference (SK hynix)

Researchers from SK hynix published a technical paper titled “StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration.” The paper proposes StreamDQ for “a lightweight architectural enhancement that enables on-the-fly dequantization in the memory subsystem for high-throughput, large-batch LLM inference,” and reports “up to 7.08× speedup and 90.23% lower energy” for mixed-precision GEMM. Find the technical paper here . July 2026. Jeong, Minki, Daegun Yoon,