  Long Short-Term Memory (LSTM) recurrent networks are frequently used for
tasks involving time-sequential data such as speech recognition. Unlike
previous LSTM accelerators that either exploit spatial weight sparsity or
temporal activation sparsity, this paper proposes a new accelerator called
"Spartus" that exploits spatio-temporal sparsity to achieve ultra-low latency
inference. Spatial sparsity is induced using a new Column-Balanced Targeted
Dropout (CBTD) structured pruning method, producing structured sparse weight
matrices for a balanced workload. The pruned networks running on Spartus
hardware achieve weight sparsity levels of up to 96% and 94% with negligible
accuracy loss on the TIMIT and the Librispeech datasets. To induce temporal
sparsity in LSTM, we extend the previous DeltaGRU method to the DeltaLSTM
method. Combining spatio-temporal sparsity with CBTD and DeltaLSTM saves on
weight memory access and associated arithmetic operations. The Spartus
architecture is scalable and supports real-time online speech recognition when
implemented on small and large FPGAs. Spartus per-sample latency for a single
DeltaLSTM layer of 1024 neurons averages 1 us. Exploiting spatio-temporal
sparsity on our test LSTM network using the TIMIT dataset leads to 46X speedup
of Spartus over its theoretical hardware performance to achieve 9.4 TOp/s
effective batch-1 throughput and 1.1 TOp/s/W power efficiency.
