arXiv subjects

Md Mizanur Rahaman Nayan

Publications and source records attributed to Md Mizanur Rahaman Nayan.

4 recordsLinked to original sources

arXiv2026

Cross-Layer Co-Designed In-Memory Hyperdimensional Computing Accelerator for Proteomics at the Edge

Database (DB) search and clustering are fundamental to data-analytics workloads such as mass spectrometry-driven proteomics. Current workflows rely on resource-intensive search and re-clustering, often offloaded to the cloud, raising privacy concerns for sensitive biological data. In this work, we enable efficient and privacy-preserving interaction on low-resource hardware platform through a cross-layer co-design approach. At the algorithm level, we propose a hardware-friendly data driven incremental update mechanism while preserving nearly the same proteomics outcome, eliminating costly repeated re-clustering during database updates. Performing cluster expansion directly during similarity search maps the algorithm naturally onto content-addressable memory (CAM), fusing distance computation and cluster expansion into one in-memory operation and eliminating dedicated distance units and data movement. This enables reduced footprint making a compact CAM-based accelerator feasible. We exploit bucket-wise search to scale and parallelize by mapping spectra buckets to CAM arrays to serve large dataset and maximize throughput, respectively. Bucket cache and main memory enables mapping datasets of different scales. Experimental results show a ~20x algorithmic speedup over re-clustering with only 0.3% additional error and 96% DB search overlap with state-of-the-art(SOTA) methods. Bucket-wise parallelization enables ~100x further acceleration over sequential search. The hardware implementation with 24.4MB SOT-MRAM CAM with 2.5MB bucket cache sustains 3.21uJ per 1K queries and 263.8uS maximum latency on a 131GB human proteome dataset.

cs.DB↗

arXiv2026

ACRONYM: Accelerated Approximate Nearest Neighbor Search in Memory for Dynamic Vector Databases

Vector database search with frequent updates is increasingly critical in applications such as retrieval augmented generation, recommendation systems, and large-scale embedding retrieval. Existing solutions, such as graph-based and partition-based approximate nearest neighbor search (ANNS), suffer from frequent index rebuilding due to data distribution-dependent indexing that impacts continuous deployment and causes long rebuilding latency. This paper proposes an algorithm-hardware co-designed platform, ACRONYM, that addresses key problems with state of the art database search. Algorithmically, it leverages efficient encoding independent of data distribution and Hamming-distance based search for efficient hardware acceleration. Architecturally, we propose CAM-based in-memory parallel distance computation followed by time multiplexed approximated top-k selection to enable the exhaustive search. We propose two-stage search that includes coarse search followed by binary refinement to achieve high recall in CAM based search which is heavily limited to small vector dimension due to capacity and wordline parasitic. ACRONYM supports continuous update without stalling and integrates novel XOR-and-Accumulate (XAC) based systolic-array encoder for efficient on chip encoding during search. Across million-scale datasets, while serving dynamic database ACRONYM achieves >90% recall at a throughput of 8e6 queries per second, with a memory footprint of only 32MB and an average energy consumption of 2.56uJ per query, speedup over HNSW (CPU) of about 400x and FAISS-IVF (GPU) of about 80x.

cs.AR↗

arXiv2025

HyDra: SOT-CAM Based Vector Symbolic Macro for Hyperdimensional Computing

Hyperdimensional computing (HDC) is a brain-inspired paradigm valued for its noise robustness, parallelism, energy efficiency, and low computational overhead. Hardware accelerators are being explored to further enhance their performance, but current solutions are often limited by application specificity and the latency of encoding and similarity search. This paper presents a generalized, reconfigurable on-chip training and inference architecture for HDC, utilizing spin-orbit-torque magnetic random access memory (SOT-MRAM) based content-addressable memory (SOT-CAM). The proposed SOT-CAM array integrates storage and computation, enabling in-memory execution of key HDC operations: binding (bitwise multiplication), permutation (bit shfiting), and efficient similarity search. Furthermore, a novel bit drop method-based permutation backed by holographic information representation of HDC is proposed which replaces conventional permutation execution in hardware resulting in a 6x latency improvement, and an HDC-specific adder reduces energy and area by 1.51X and 1.43x, respectively. To mitigate the parasitic effect of interconnects in the similarity search, a four-stage voltage scaling scheme has been proposed to ensure an accurate representation of the Hamming distance. Benchmarked at 7nm, the architecture achieves energy reductions of 21.5x, 552.74x, 1.45x, and 282.57x for addition, permutation, multiplication, and search operations, respectively, compared to CMOS-based HDC. Against state-of-the-art HDC accelerators, it achieves a 2.27x lower energy consumption and outperforms CPU and eGPU implementations by 2702x and 23161x, respectively, with less than 3% drop in accuracy.

cs.ET↗

arXiv2025

Axon: A novel systolic array architecture for improved run time and energy efficient GeMM and Conv operation with on-chip im2col

General matrix multiplication (GeMM) is a core operation in virtually all AI applications. Systolic array (SA) based architectures have shown great promise as GeMM hardware accelerators thanks to their speed and energy efficiency. Unfortunately, SAs incur a linear delay in filling the operands, due to unidirectional propagation via pipeline latches. In this work, we propose a novel in-array data orchestration technique in SAs where we enable data feeding on the principal diagonal followed by bi-directional propagation. This improves the runtime by up to 2X at minimal hardware overhead. In addition, the proposed data orchestration enables convolution lowering (known as im2col) using a simple hardware support to fully exploit input feature map reuse opportunity and significantly lower the off-chip memory traffic resulting in 1.2X throughput improvement and 2.17X inference energy reduction during YOLOv3 and RESNET50 workload on average. In contrast, conventional data orchestration would require more elaborate hardware and control signals to implement im2col in hardware because of the data skew. We have synthesized and conducted place and route for 16X16 systolic arrays based on the novel and conventional orchestrations using ASAP 7nm PDK and found that our proposed approach results in 0.211% area and 1.6% power overheads.

cs.AR↗