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Co-Design for Efficient & Adaptive ML / Umuroglu, Yaman (speaker) (AMD Research & Advanced Development)
Beyond the well-known highlights in computer vision and natural language, AI is steadily expanding into new application domains. This Pervasive AI trend requires supporting diverse and fast-moving application requirements, ranging from specialized I/O to fault tolerance and limited resources, all the while retaining high performance and low latency. [...]
2024 - 3545. EP-IT Data Science Seminars External link: Event details In : Co-Design for Efficient & Adaptive ML

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Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark / Borras, Hendrik (Heidelberg U.) ; Di Guglielmo, Giuseppe (Columbia U.) ; Duarte, Javier (UC, San Diego) ; Ghielmetti, Nicolò (CERN) ; Hawks, Ben (Fermilab) ; Hauck, Scott (Washington U., Seattle) ; Hsu, Shih-Chieh (Washington U., Seattle) ; Kastner, Ryan (UC, San Diego) ; Liang, Jason (UC, San Diego) ; Meza, Andres (UC, San Diego) et al.
We present our development experience and recent results for the MLPerf Tiny Inference Benchmark on field-programmable gate array (FPGA) platforms. [...]
arXiv:2206.11791 ; FERMILAB-CONF-22-479-SCD. -
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QONNX: Representing Arbitrary-Precision Quantized Neural Networks / Pappalardo, Alessandro (Unlisted, IE) ; Umuroglu, Yaman (Unlisted, IE) ; Blott, Michaela (Unlisted, IE) ; Mitrevski, Jovan (Fermilab) ; Hawks, Ben (Fermilab) ; Tran, Nhan (Fermilab) ; Loncar, Vladimir (MIT, LNS) ; Summers, Sioni (CERN) ; Borras, Hendrik (U. Heidelberg) ; Muhizi, Jules (Harvard U. (main)) et al.
We present extensions to the Open Neural Network Exchange (ONNX) intermediate representation format to represent arbitrary-precision quantized neural networks. [...]
arXiv:2206.07527 ; FERMILAB-CONF-22-471-SCD. - 9 p.
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