Aceleração de hardware em sistemas embarcados para aprendizado de máquina utilizando KNN em FPGA

Alessandro CopettiWanderson Berbert


Aprendizado de máquina tem se tornado uma ferramenta essencialpara qualquer sistema de tomada de decisão. Devido a limitaçõesde performance impostas por arquiteturas tradicionais que utilizamCentral Processing Units (CPUs), para aplicações mais críticas, métodosde aceleração com Graphical Processing Unit (GPU) e ApplicationSpecific Integrated Circuit (ASIC) têm sido empregados. No entanto,quando aplicadas a sistemas embarcados, estas apresentam limitaçõesrelacionadas a tamanho físico e complexidade. Para resolverestes problemas, a utilização da tecnologia Field Programmable GateArray (FPGA) tem se mostrado promissora devido a sua grande eficiência,paralelismo real, reconfigurabilidade e flexibilidade. Diantedisso, este estudo tem como objetivo, além de fazer uma revisãoaprofundada da bibliografia, apresentar arquiteturas projetadasem FPGA que buscam minimizar tais limitações, maximizando aeficiência, sem perda de performance significativa e de modo a viabilizarsua utilização em sistemas embarcados. Resultados mostramganhos em performance acima de 95% quando utilizando um hardwareespecialista desenvolvido em FPGA utilizando o algoritmo deaprendizado de máquina K-Nearest Neighbor (KNN).

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