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Found 5 papers, 4 papers with code
Hermes: Accelerating Long-Latency Load Requests via Perceptron-Based Off-Chip Load Prediction
To this end, we propose a new technique called Hermes, whose key idea is to: 1) accurately predict which load requests might go off-chip, and 2) speculatively fetch the data required by the predicted off-chip loads directly from the main memory, while also concurrently accessing the cache hierarchy for such loads.
EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators
We find that commonly-used low-power CNN inference accelerators based on spatial architectures are not optimized for both of these convolutional kernels.
BLEND: A Fast, Memory-Efficient, and Accurate Mechanism to Find Fuzzy Seed Matches in Genome Analysis
We introduce BLEND, the first efficient and accurate mechanism that can identify both exact-matching and highly similar seeds with a single lookup of their hash values, called fuzzy seed matches.
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.
EDEN: Enabling Energy-Efficient, High-Performance Deep Neural Network Inference Using Approximate DRAM
Based on this observation, we propose EDEN, a general framework that reduces DNN energy consumption and DNN evaluation latency by using approximate DRAM devices, while strictly meeting a user-specified target DNN accuracy.
