Machine learning systems : the role of hardware design for dependable computing /
This book addresses the technological contributions and developments of advanced hardware for Machine Learning (ML) computing systems. The authors discuss state-of-the-art progress in this area (and related topics) as well as reporting on their application to diverse fields. This is achieved by chap...
| Other Authors: | , , , |
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| Format: | eBook |
| Language: | English |
| Published: |
Cham :
Springer,
[2026]
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| Series: | Engineering Series.
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| Subjects: |
Table of Contents:
- High performance machine learning accelerators on fpga
- Floating point arithmetic in deep neural networks evaluation and implementation of conventional and emerging formats with mixed precision strategies
- High performance computing architectures for ml
- High performance domain specific computing architectures for machine learning
- Edge ai training accelerator design
- Accelerating machine learning with unconventional architectures
- Mram based energy efficient computing architectures for machine learning accelerators
- Energy efficient data aware computation in computing in memory architecture
- Stochastic computing applied to morphological neural networks
- Approximate computing in machine learning - More than you bargained for
- Edge computing meets giant ai innovations in large language model efficiency
- Chiplet based accelerator design for scalable training of transformer based generative adversarial networks
- Energy consumption in generative ai insights from large language models inference
- Towards sustainable and responsible gen ai an investigation on energy efficient computing and cost effective complementary components
- Low power machine learning realization techniques on biomedical wearable devices
- Application of algorithm and hardware co design in the hardware accelerator of visual slam front end
- Application of Aagorithm and hardware co design in the hardware accelerator of visual slam back end
- Hardware efficient designs for spiking neural networks
- Understanding neural network fault tolerance from the analog hardware to the gpu
- On the use of ml techniques in safety critical systems
- Fault injection and tolerance techniques for deep learning models deployed on sram based fpgas
- Dependability evaluation of parameters and variables in large language models llms to soft errors on memory
- Lightweight algorithm based fault tolerance abft for resilient ml systems
- Using error correction code schemes in dependable machine learning systems
- Analog error correcting codes for dependable in memory computing of neural networks
- Perturbation based error tolerance for large scale networks
- Quantization aided cost efficient reliability of cnn accelerators for edge ai
- Trustworthy ai at the cloud
- Exploring hardware driven privacy techniques for trustworthy machine learning
- Machine learning and hardware security the role of ai for hardware in the security era
- Machine learning systems for high performance and dependability the role of fpga design.