Titlebook: Deep Learning Systems; Algorithms, Compiler Andres Rodriguez Book 2021 Springer Nature Switzerland AG 2021

DIS

脆弱帶來

1935-3235 models for commercial applications. The exponential growth in computational power is slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL) workloads is rapidly growing. Model size, serving latency, and power constraints are a significant challenge in the deplo

Indecisive

Design of Jigs, Fixtures and Press Toolscing the model size, and evaluating the trained model. The training process can be computational and memory intensive, and there are techniques discussed in this and the next two chapters to reduce the training time and mitigate memory bottlenecks.

GUILT

迎合

https://doi.org/10.1007/978-94-009-4626-2ware designers can pack more smaller numerical format multipliers into a given die area to improve the computational performance. However, using a smaller numerical representation may result in lower statistical performance for some models.

hemoglobin

材料等

Ethics

Book 2021ists; (2) hardware designers that develop specialized hardware to accelerate the components in the DL models; and (3) performance and compiler engineers that optimize software to run more efficiently on a given hardware. Hardware engineers should be aware of the characteristics and components of pro

館長

Building Blocks,nt neural networks (RNNs), and transformer-based topologies. These topologies are . with nodes and edges, where a node represents an operator, and an edge represents a data-dependency between the nodes, as shown in Figure 1.5.

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