The Magnificent Seven Challenges and Opportunities in Domain-Specific Accelerator Design for Autonomous Systems
July 24, 2024 Β· Declared Dead Β· π Design Automation Conference
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Sabrina M. Neuman, Brian Plancher, Vijay Janapa Reddi
arXiv ID
2407.17311
Category
cs.AR: Hardware Architecture
Cross-listed
cs.RO,
eess.SY
Citations
1
Venue
Design Automation Conference
Last Checked
3 months ago
Abstract
The end of Moore's Law and Dennard Scaling has combined with advances in agile hardware design to foster a golden age of domain-specific acceleration. However, this new frontier of computing opportunities is not without pitfalls. As computer architects approach unfamiliar domains, we have seen common themes emerge in the challenges that can hinder progress in the development of useful acceleration. In this work, we present the Magnificent Seven Challenges in domain-specific accelerator design that can guide adventurous architects to contribute meaningfully to novel application domains. Although these challenges appear across domains ranging from ML to genomics, we examine them through the lens of autonomous systems as a motivating example in this work. To that end, we identify opportunities for the path forward in a successful domain-specific accelerator design from these challenges.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Hardware Architecture
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Corona: System Implications of Emerging Nanophotonic Technology
R.I.P.
π»
Ghosted
A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)
R.I.P.
π»
Ghosted
SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning
R.I.P.
π»
Ghosted
Neural Cache: Bit-Serial In-Cache Acceleration of Deep Neural Networks
R.I.P.
π»
Ghosted
SpArch: Efficient Architecture for Sparse Matrix Multiplication
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted
Explanation in Artificial Intelligence: Insights from the Social Sciences
R.I.P.
π»
Ghosted