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Found 4 papers, 2 papers with code
Reinforcement Learning Approach for Mapping Applications to Dataflow-Based Coarse-Grained Reconfigurable Array
In this work we propose a Reinforcement Learning framework with Global Graph Attention (GGA) module and output masking of invalid placements to find and optimize instruction schedules.
Snowflake: A Model Agnostic Accelerator for Deep Convolutional Neural Networks
Snowflake is able to achieve a computational efficiency of over 91% on modern CNN models.
Hardware Architecture
Compiling Deep Learning Models for Custom Hardware Accelerators
Given a programmable hardware accelerator with a CNN oriented custom instructions set, the compiler's task is to exploit the hardware's full potential, while abiding with the hardware constraints and maintaining generality to run different CNN models with varying workload properties.
Recurrent Neural Networks Hardware Implementation on FPGA
Recurrent Neural Networks (RNNs) have the ability to retain memory and learn data sequences.
