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Recurrent neural networks such as Long Short-Term Memories (LSTMs) learn temporal dependencies by keeping an internal state, making them ideal for time-series problems such as speech recognition. However, the output-to-input feedback creates distinctive memory bandwidth and scalability challenges in designing accelerators for RNNs. We present Muntaniala, an RNN accelerator architecture for LSTM inference with a silicon-measured energy-efficiency of 3.25TOP/s/W and performance of 30.53GOP/s in UMC 65nm technology. The scalable design of Muntaniala allows running large RNN models by combining multiple tiles in a systolic array. We keep all parameters stationary on every die in the array, drastically reducing the I/O communication to only loading new features and sharing partial results with other dies. For quantifying the overall system power, including I/O power, we built Vau da Muntanialas, to the best of our knowledge, the first demonstration of a systolic multi-chip-on-PCB array of RNN accelerator. Our multi-die prototype performs LSTM inference with 192 hidden states in 330 <inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> with a total system power of 9.0 mW at 10 MHz consuming 2.95 <inline-formula> <tex-math notation="LaTeX">$\mu \text{J}$ </tex-math></inline-formula>. Targeting the 8/16-bit quantization implemented in Muntaniala, we show a phoneme error rate (PER) drop of approximately 3&#x0025; with respect to floating-point (FP) on a 3L-384NH-123NI LSTM network on the TIMIT dataset.

Vau Da Muntanialas: Energy-Efficient Multi-Die Scalable Acceleration of RNN Inference