Publications by Author: Colby Banbury

M. Mazumder, C. Banbury, J. Meyer, P. Warden, and V. J. Reddi, “Few-Shot Keyword Spotting in Any Language,” in INTERSPEECH 2021, Virtual, Brno, Czech Republic, 2021. arXiv VersionAbstract
We introduce a few-shot transfer learning method for keyword spotting in any language. Leveraging open speech corpora in nine languages, we automate the extraction of a large multilingual keyword bank and use it to train an embedding model. With just five training examples, we fine-tune the embedding model for keyword spotting and achieve an average F1 score of 0.75 on keyword classification for 180 new keywords unseen by the embedding model in these nine languages. This embedding model also generalizes to new languages. We achieve an average F1 score of 0.65 on 5-shot models for 260 keywords sampled across 13 new languages unseen by the embedding model. We investigate streaming accuracy for our 5-shot models in two contexts: keyword spotting and keyword search. Across 440 keywords in 22 languages, we achieve an average streaming keyword spotting accuracy of 85.2% with a false acceptance rate of 1.2%, and observe promising initial results on keyword search.
M. Lam, Z. Yedidia, C. Banbury, and V. J. Reddi, “PrecisionBatching: Bitserial Decomposition for Efficient Neural Network Inference on GPUs,” in The 30th International Conference on Parallel Architectures and Compilation Techniques, presented at PACT 2021, virtual, September 26-29, 2021. IEEE VersionAbstract
We present PrecisionBatching, a quantized inference algorithm for speeding up neural network inference on traditional hardware platforms at low bitwidths. PrecisionBatching is based on the following insights: 1) neural network inference with low batch sizes on traditional hardware architectures (e.g: GPUs) is memory bound, 2) activation precision is critical to improving quantized model quality and 3) matrix-vector multiplication can be decomposed into binary matrix-matrix multiplications, enabling quantized inference with higher precision activations at the cost of more arithmetic operations. Combining these three insights, PrecisionBatching enables inference at extreme quantization levels (< 8 bits) by shifting a memory bound problem to a compute bound problem and achieves higher compute efficiency and runtime speedup at fixed accuracy thresholds against standard quantized inference methods. Across a variety of applications (MNIST, language modeling, natural language inference, reinforcement learning) and neural network architectures (fully connected, RNN, LSTM), PrecisionBatching yields end-to-end speedups of over 8x on a GPU within a < 1-5% error margin of the full precision baseline, outperforming traditional 8-bit quantized inference by over 1.5x-2x at the same error tolerance.
PDF Version
M. Lam, Z. Yedidia, C. Banbury, and V. J. Reddi, “Quantized Neural Network Inference with Precision Batching”. 2020.