Efficient Weight Reuse for Large LSTMs. Abstract: Long Short-Term Memory (LSTM) networks have been deployed in speech recognition, natural language processing ...
In this work, we focus on LSTM models which are too large to store in on-chip memory of FPGA and we propose a novel blocking-batching strategy splitting the ...
Abstract—Long Short-Term Memory (LSTM) networks have been deployed in speech recognition, natural language processing and financial calculations in recent ...
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A stall-free hardware architecture is proposed by reorganising the order of operations in an LSTM system and a unique blocking-batching strategy to reuse ...
Efficient Weight Reuse for Large LSTMs. Zhiqiang Que1, Thomas Nugent1 ... - reuse weights for large LSTM systems. • Stall-free architecture (address C1).
Jul 9, 2020 · A new blocking-batching strategy reusing the LSTM weights to optimize the throughput of large LSTM systems on FPGAs. ... Efficient Weight Reuse ...
Some works store weights in off-chip memory and reduce bandwidth requirements through data reuse [16, 19] . The authors of [19] split the weight matrix into ...
1) A new blocking-batching strategy to reuse the LSTM weights to optimise the throughput of large LSTM sys- · 2) A novel stall-free hardware architecture to ...
A novel hardware architecture to overcome data dependency and a new blocking-batching strategy to reuse the LSTM weights fetched from external memory to ...
We propose a novel hardware architecture to overcome data dependency and a new blocking-batching strategy to reuse the LSTM weights fetched from external memory ...