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Schrödinger's FP: Dynamic Adaptation of Floating-Point Containers for Deep Learning Training
Quantum Mantissa and Quantum Exponent are machine learning compression methods that tap into the gradient descent algorithm to learn the minimal mantissa and exponent bitlengths on a per-layer granularity.
Mokey: Enabling Narrow Fixed-Point Inference for Out-of-the-Box Floating-Point Transformer Models
Mokey reduces the footprint of state-of-the-art 32-bit or 16-bit floating-point transformer models by quantizing all values to 4-bit indexes into dictionaries of representative 16-bit fixed-point centroids.
FPRaker: A Processing Element For Accelerating Neural Network Training
We demonstrate that FPRaker can be used to compose an accelerator for training and that it can improve performance and energy efficiency compared to using conventional floating-point units under ISO-compute area constraints.
TensorDash: Exploiting Sparsity to Accelerate Deep Neural Network Training and Inference
TensorDash is a hardware level technique for enabling data-parallel MAC units to take advantage of sparsity in their input operand streams.
GOBO: Quantizing Attention-Based NLP Models for Low Latency and Energy Efficient Inference
Second, we present a co-designed hardware architecture that also reduces computation.
