English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Food profiling is an essential step in any food monitoring system needed toprevent health risks and potential frauds in the food industry. Significantimprovements in sequencing technologies are pushing food profiling to becomethe main computational bottleneck. State-of-the-art profilers are unfortunatelytoo costly for food profiling.  Our goal is to design a food profiler that solves the main limitations ofexisting profilers, namely (1) working on massive data structures and (2)incurring considerable data movement for a real-time monitoring system. To thisend, we propose Demeter, the first platform-independent framework for foodprofiling. Demeter overcomes the first limitation through the use ofhyperdimensional computing (HDC) and efficiently performs the accuratefew-species classification required in food profiling. We overcome the secondlimitation by using an in-memory hardware accelerator for Demeter (namedAcc-Demeter) based on memristor devices. Acc-Demeter actualizes severaldomain-specific optimizations and exploits the inherent characteristics ofmemristors to improve the overall performance and energy consumption ofAcc-Demeter.  We compare Demeter's accuracy with other industrial food profilers usingdetailed software modeling. We synthesize Acc-Demeter's required hardware usingUMC's 65nm library by considering an accurate PCM model based on silicon-basedprototypes. Our evaluations demonstrate that Acc-Demeter achieves a (1)throughput improvement of 192x and 724x and (2) memory reduction of 36x and 33xcompared to Kraken2 and MetaCache (2 state-of-the-art profilers), respectively,on typical food-related databases. Demeter maintains an acceptable profilingaccuracy (within 2% of existing tools) and incurs a very low area overhead.

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