The standard-cell placement legalization is a critical step in the physical design. The emerging 3D ICs have brought challenges to traditional legalizers on efficiency and effectiveness. In this work, we present a fast flow-based legalization algorithm, 3D-Flow, that minimizes cell displacement in a 3D solution space. Our legalizer resolves overflowed bins by finding the shortest augmenting path on a 3D grid graph, utilizing an effective branch-and-bound algorithm. Moreover, a post-optimization with a cycle-canceling algorithm is proposed to minimize the maximum displacement. Our approach leverages the global perspective inherent in network flow methods, considering multiple dies in 3D ICs to minimize cell displacement. Experimental results on ICCAD 2022 and 2023 contest benchmarks demonstrate our proposed algorithm achieves up to 13% and 43% less average and maximum cell displacement compared to state-of-the-art legalizers in a similar runtime.
Vector similarity search plays a pivotal role in modern applications, including recommendation systems, image search, large language models (LLMs), and high-dimensional data retrieval. As data size scales, our research reveals that the search phase imposes substantial demands on DRAM bandwidth, leading to performance limitations in conventional von Neumann architecture with shared memory buses. This data movement bottleneck restricts the efficiency and scalability of vector similarity search due to insufficient memory bandwidth. To mitigate this issue, we leverage UPMEM, an off-the-shelf near-memory processing (NMP) system, to minimize the data movement between memory and compute units. However, UPMEM’s computing engine has certain limitations and requires thorough application integration to unleash its high-parallelism capabilities. In this work, we introduce UPMEM-aware Vector Similarity Search (UPVSS), an architecture-aware system that jointly manages vector similarity search and UPMEM’s NMP technology. UPVSS prioritizes offloading operations based on their strengths and capabilities, effectively alleviating the data movement bottleneck and improving overall system performance.
