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Found 9 papers, 3 papers with code
CiMNet: Towards Joint Optimization for DNN Architecture and Configuration for Compute-In-Memory Hardware
With the recent growth in demand for large-scale deep neural networks, compute in-memory (CiM) has come up as a prominent solution to alleviate bandwidth and on-chip interconnect bottlenecks that constrain Von-Neuman architectures.
Reclaimer: A Reinforcement Learning Approach to Dynamic Resource Allocation for Cloud Microservices
Many cloud applications are migrated from the monolithic model to a microservices framework in which hundreds of loosely-coupled microservices run concurrently, with significant benefits in terms of scalability, rapid development, modularity, and isolation.
VEGETA: Vertically-Integrated Extensions for Sparse/Dense GEMM Tile Acceleration on CPUs
Therefore, as DL workloads embrace sparsity to reduce the computations and memory size of models, it is also imperative for CPUs to add support for sparsity to avoid under-utilization of the dense matrix engine and inefficient usage of the caches and registers.
ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis
We identify an urgent need for a flexible, high-performance, and energy-efficient HW/SW co-design to address the major inefficiencies in the Baum-Welch algorithm for pHMMs.
Segment-Fusion: Hierarchical Context Fusion for Robust 3D Semantic Segmentation
3D semantic segmentation is a fundamental building block for several scene understanding applications such as autonomous driving, robotics and AR/VR.
Robust 3D Scene Segmentation through Hierarchical and Learnable Part-Fusion
3D semantic segmentation is a fundamental building block for several scene understanding applications such as autonomous driving, robotics and AR/VR.
RASA: Efficient Register-Aware Systolic Array Matrix Engine for CPU
As AI-based applications become pervasive, CPU vendors are starting to incorporate matrix engines within the datapath to boost efficiency.
Pythia: A Customizable Hardware Prefetching Framework Using Online Reinforcement Learning
In this paper, we make a case for designing a holistic prefetch algorithm that learns to prefetch using multiple different types of program context and system-level feedback information inherent to its design.
GenASM: A High-Performance, Low-Power Approximate String Matching Acceleration Framework for Genome Sequence Analysis
Unfortunately, it is currently bottlenecked by the computational power and memory bandwidth limitations of existing systems, as many of the steps in genome sequence analysis must process a large amount of data.
Hardware Architecture Genomics
