تقرير
Fully-Binarized, Parallel, RRAM-based Computing Primitive for In-Memory Similarity Search
| العنوان: | Fully-Binarized, Parallel, RRAM-based Computing Primitive for In-Memory Similarity Search |
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| المؤلفون: | Kingra, Sandeep Kaur, Parmar, Vivek, Verma, Deepak, Bricalli, Alessandro, Piccolboni, Giuseppe, Molas, Gabriel, Regev, Amir, Suri, Manan |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Physics (Other) |
| مصطلحات موضوعية: | Computer Science - Emerging Technologies, Physics - Applied Physics |
| الوصف: | In this work, we propose a fully-binarized XOR-based IMSS (In-Memory Similarity Search) using RRAM (Resistive Random Access Memory) arrays. XOR (Exclusive OR) operation is realized using 2T-2R bitcells arranged along the column in an array. This enables simultaneous match operation across multiple stored data vectors by performing analog column-wise XOR operation and summation to compute HD (Hamming Distance). The proposed scheme is experimentally validated on fabricated RRAM arrays. Full-system validation is performed through SPICE simulations using open source Skywater 130 nm CMOS PDK demonstrating energy of 17 fJ per XOR operation using the proposed bitcell with a full-system power dissipation of 145 $\mu$W. Using projected estimations at advanced nodes (28 nm) energy savings of $\approx$1.5$\times$ compared to the state-of-the-art can be observed for a fixed workload. Application-level validation is performed on HSI (Hyper-Spectral Image) pixel classification task using the Salinas dataset demonstrating an accuracy of 90%. |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1109/TCSII.2022.3207378 |
| URL الوصول: | http://arxiv.org/abs/2208.02651 |
| رقم الأكسشن: | edsarx.2208.02651 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1109/TCSII.2022.3207378 |
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