Hardware Acceleration for Neural Networks: A Survey
Paper#Hardware Acceleration, Deep Learning, Neural Networks, LLMs🔬 Research|Analyzed: Jan 3, 2026 15:58•
Published: Dec 30, 2025 00:27
•1 min read
•ArXivAnalysis
This survey paper provides a comprehensive overview of hardware acceleration techniques for deep learning, addressing the growing importance of efficient execution due to increasing model sizes and deployment diversity. It's valuable for researchers and practitioners seeking to understand the landscape of hardware accelerators, optimization strategies, and open challenges in the field.
Key Takeaways
- •Provides a comprehensive overview of hardware acceleration techniques for deep learning.
- •Covers a wide range of hardware architectures, including GPUs, TPUs, FPGAs, and ASICs.
- •Discusses various optimization levers such as reduced precision, sparsity, and operator fusion.
- •Highlights open challenges in the field, including efficient LLM inference and support for dynamic workloads.
Reference / Citation
View Original"The survey reviews the technology landscape for hardware acceleration of deep learning, spanning GPUs and tensor-core architectures; domain-specific accelerators (e.g., TPUs/NPUs); FPGA-based designs; ASIC inference engines; and emerging LLM-serving accelerators such as LPUs (language processing units), alongside in-/near-memory computing and neuromorphic/analog approaches."