MIT深度神经网络硬件架构设计教程

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MIT EYERISS芯片设计指导教程,基于FPGA/ASIC神经网络设计的干货
Outline Overview of Deep Neural Networks DNN Development Resources Survey of DNN Computation DNN Accelerators Network Optimizations Benchmarking Metrics for Evaluation DNN Training Il O Participant Takeaways Understand the key design considerations for DNNS Be able to evaluate different implementations of DNN with benchmarks and comparison metrics Understand the tradeoffs between various architectures and platforms Assess the utility of various optimization approaches Understand recent implementation trends and opportunities Il O Background of Deep Neural Networks Il O Al and Machine Learning Artificial Intelligence Machine Learning Field of study that gives computers the ability to learn without being explicitly programmed Arthur samuel. 1959 Il O Brain-Inspired Machine Learning Artificial Intelligence Machine Learning Brain-Inspired An algorithm that takes its basic functionality from our understanding of how the brain operates Il O How Does the brain work? pulses carried toward cell bod branches dendrites of axon Ms/terminals axon nucleus axon impulses carried away from cell body cell body The basic computational unit of the brain is a neuron >86b neurons in the brain Neurons are connected with nearly 1014-1015 synapses Neurons receive input signal from dendrites and produce output signal along axon, which interact with the dendrites of other neurons via synaptic weights Synaptic weights-learnable control influence strength Il O Image Source: Stanford Spiking-based Machine Learning Artificial Intelligence Machine Learning Brain-Inspired Spiking Il O Spiking Architecture Brain-inspired Integrate and fire EXample: IBM TrueNorth C 20 Internal State …D TrueNcrth Chip 14 3 84 cr05 Synapse B [Merolla et al., Science 2014; Esser et aL., PNAS 2016 Il O http://www.research.ibmcom/articles/brain-chip.shtml 10

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