Low-Current-Density Magnetic Tunnel Junctions for STT-RAM Application Using MgO$_{{x}}$ N$_{\text{1}-{x}}\,\,({x}=\text{0.57}$ ) Tunnel Barrier

Moinuddin, Mohamad G.; Lone, Aijaz H.; Shringi, S.N.; Srinivasan, Srikant; Sharma, Satinder K.

doi:10.1109/ted.2019.2954131

Cited by 11 publications

(1 citation statement)

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“…This growing interest in using CIM for resolving the shortcomings of DNNs is driven by two main factors: (1) the potential of the CIM paradigm to process data where it resides to reduce the large performance and energy overheads of data movement and (2) the analog operational properties of these nanoscale emerging technologies (e.g., memristors) that intrinsically support efficient Vector-Matrix-Multiplication (VMM), multiple of which are used to implement a Matrix-Matrix-Multiplication (MMM) that is the most dominant operation in DNNs. However, the memristor-based CIM solutions for basecalling can greatly degrade the DNN inference accuracy due to (1) the limited quantization levels supported by memristor devices [27,111] and (2) non-idealities of memristive devices and circuits used to adopt memristor-based memory arrays, such as sneak paths [48,118] and the non-linearity of peripheral circuitry [58,83,147]. To propose viable solutions for accelerating the large-scale DNN-based basecallers, these aspects must be considered at all computing stack layers, i.e., application, architecture, and device.…”

Section: Introductionmentioning

confidence: 99%

Demeter: A Fast and Energy-Efficient Food Profiler Using Hyperdimensional Computing in Memory

et al. 2022

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Food profiling is an essential step in any food monitoring system needed to prevent health risks and potential frauds in the food industry. Significant improvements in sequencing technologies are pushing food profiling to become the main computational bottleneck. State-of-the-art profilers are unfortunately too costly for food profiling. Our goal is to design a food profiler that solves the main limitations of existing profilers, namely (1) working on massive data structures and (2) incurring considerable data movement, for a real-time monitoring system. To this end, we propose Demeter, the first platform-independent framework for food profiling. Demeter overcomes the first limitation through the use of hyperdimensional computing (HDC) and efficiently performs the accurate few-species classification required in food profiling. We overcome the second limitation by the use of an in-memory hardware accelerator for Demeter (named Acc-Demeter) based on memristor devices. Acc-Demeter actualizes several domain-specific optimizations and exploits the inherent characteristics of memristors to improve the overall performance and energy consumption of Acc-Demeter. We compare Demeter's accuracy with other industrial food profilers using detailed software modeling. We synthesize Acc-Demeter's required hardware using UMC's 65nm library by considering an accurate PCM model based on silicon-based prototypes. Our evaluations demonstrate that Acc-Demeter achieves a (1) throughput improvement of 192× and 724× and (2) memory reduction of 36× and 33× compared to Kraken2 and MetaCache (2 state-of-the-art profilers), respectively, on typical food-related databases. Demeter maintains an acceptable profiling accuracy (within 2% of existing tools) and incurs a very low area overhead.

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