ReRAM-based 4T2R nonvolatile TCAM with 7x NVM-stress reduction, and 4x improvement in speed-wordlength-capacity for normally-off instant-on filter-based search engines used in big-data processing

Huang, Li Yue; Chang, M.-S.; Chuang, Ching Hao; Kuo, C.-C. Jay; Chen, C. F.; Yang, Geng Hau; Tsai, Hsiang Jen; Chen, Tien-Fu; Sheu, Shyh Shyuan; Su, Keng Li; Chen, Frederick T.; Ku, T. K.; Tsai, M. J.; Kao, Mu‐Jung

doi:10.1109/vlsic.2014.6858404

Cited by 16 publications

(5 citation statements)

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“…Thus, to attain fast parallel memory operations, reduced area and low-energy consumption, RRAM-based non-volatile SRAM (nvSRAM) was proposed [174] in which two RRAM cells are stacked on eight transistors, forming an 8T2R structure. Also, nonvolatile ternary content-addressable memory (TCAM) having 4T2R cell structure [175] and non-volatile flip flops having reduced stress time and write power based on RRAM have been demonstrated recently [176].…”

Section: Non-volatile Srammentioning

confidence: 99%

Resistive Random Access Memory (RRAM): an Overview of Materials, Switching Mechanism, Performance, Multilevel Cell (mlc) Storage, Modeling, and Applications

2020

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In this manuscript, recent progress in the area of resistive random access memory (RRAM) technology which is considered one of the most standout emerging memory technologies owing to its high speed, low cost, enhanced storage density, potential applications in various fields, and excellent scalability is comprehensively reviewed. First, a brief overview of the field of emerging memory technologies is provided. The material properties, resistance switching mechanism, and electrical characteristics of RRAM are discussed. Also, various issues such as endurance, retention, uniformity, and the effect of operating temperature and random telegraph noise (RTN) are elaborated. A discussion on multilevel cell (MLC) storage capability of RRAM, which is attractive for achieving increased storage density and low cost is presented. Different operation schemes to achieve reliable MLC operation along with their physical mechanisms have been provided. In addition, an elaborate description of switching methodologies and current voltage relationships for various popular RRAM models is covered in this work. The prospective applications of RRAM to various fields such as security, neuromorphic computing, and non-volatile logic systems are addressed briefly. The present review article concludes with the discussion on the challenges and future prospects of the RRAM.

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Section: Non-volatile Srammentioning

confidence: 99%

Resistive Random Access Memory (RRAM): an Overview of Materials, Switching Mechanism, Performance, Multilevel Cell (mlc) Storage, Modeling, and Applications

2020

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“…Table 2: DASH-CAM area, power, and latency, compared with HD-CAM [15],EDAM [20] and 1R3T resistive TCAM [10]. Based on timing results of extensive Monte-Carlo simulations, DASH-CAM can be operated at 1GHz.…”

Section: Power Consumption Silicon Area and Speedupmentioning

confidence: 99%

“…DASH-CAM operates at 700 mV and consumes an average of 13.5fJ per 32-cell row. These results are summarized in Table 2, and compared with prior art designs for 𝑘-mer or pattern matching: HD-CAM [15], EDAM [20] and 1R3T resistive TCAM [10]. The main advantage of DASH-CAM over HD-CAM and EDAM is its density, which enables efficient classification of larger genomes, such as bacterial pathogens.…”

Section: Power Consumption Silicon Area and Speedupmentioning

confidence: 99%

DASH-CAM: Dynamic Approximate SearcH Content Addressable Memory for genome classification

Jahshan,

Merlin,

Garzón

et al. 2023

Preprint

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We propose a novel dynamic storage-based approximate search content addressable memory (DASH-CAM) for computational genomics applications, particularly for identification and classification of viral pathogens of epidemic significance. DASH-CAM provides 5.5× better density compared to state-of-the-art SRAM-based approximate search CAM. This allows using DASH-CAM as a portable classifier that can be applied to pathogen surveillance in low-quality field settings during pandemics, as well as to pathogen diagnostics at points of care. DASH-CAM approximate search capabilities allow a high level of flexibility when dealing with a variety of industrial sequencers with different error profiles. DASH-CAM achieves up to 30% and 20% higherF1score when classifying DNA reads with 10% error rate, compared to state-of-the-art DNA classification tools MetaCache-GPU and Kraken2 respectively. Simulated at 1GHz, DASH-CAM provides 1, 178× and 1, 040× average speedup over MetaCache-GPU and Kraken2 respectively.CCS CONCEPTS•Hardware→Bio-embedded electronics.

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“…7(d) shows the circuit of a ReRAM CAM cell [47] consisting of two ReRAMs and two comparison transistors (M1/M2), a write-control transistor (M3), and an ML-driver transistor (M4). The gate and source of the comparison transistor are connected to datalines (DL/DLB) and a dynamic sourceline (DSL), respectively.…”

Section: A Types Of Nano-camsmentioning

confidence: 99%

Emerging Trends in Design and Applications of Memory-Based Computing and Content-Addressable Memories

Karam

Puri

Ghosh³

et al. 2015

Proc. IEEE

170

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ReRAM-based 4T2R nonvolatile TCAM with 7x NVM-stress reduction, and 4x improvement in speed-wordlength-capacity for normally-off instant-on filter-based search engines used in big-data processing

Cited by 16 publications

References 0 publications

Resistive Random Access Memory (RRAM): an Overview of Materials, Switching Mechanism, Performance, Multilevel Cell (mlc) Storage, Modeling, and Applications

Resistive Random Access Memory (RRAM): an Overview of Materials, Switching Mechanism, Performance, Multilevel Cell (mlc) Storage, Modeling, and Applications

DASH-CAM: Dynamic Approximate SearcH Content Addressable Memory for genome classification

Emerging Trends in Design and Applications of Memory-Based Computing and Content-Addressable Memories

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