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Found 7 papers, 0 papers with code
Schrödinger's FP: Dynamic Adaptation of Floating-Point Containers for Deep Learning Training
We introduce a software-hardware co-design approach to reduce memory traffic and footprint during training with BFloat16 or FP32 boosting energy efficiency and execution time performance.
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.
APack: Off-Chip, Lossless Data Compression for Efficient Deep Learning Inference
When integrated with a Tensorcore-based accelerator, APack boosts the speedup and energy efficiency to 1. 44X and 1. 37X respectively.
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.
Laconic Deep Learning Computing
A Laconic configuration that uses a 1K-wire weight memory interface, outperforms the 2K-wire conventional accelerator by 15. 4x and is 1. 95x more energy efficient.
Bit-Tactical: Exploiting Ineffectual Computations in Convolutional Neural Networks: Which, Why, and How
We show that, during inference with Convolutional Neural Networks (CNNs), more than 2x to $8x ineffectual work can be exposed if instead of targeting those weights and activations that are zero, we target different combinations of value stream properties.
