80 citations · 142 across the 9 of their papers we have counts for
11 papers
Spatons: Spatiotonally Localized Building Blocks for Efficient XR Predistortion
Mohammadjavad Maheronnaghsh, Sushant Kondguli, Steve Mann +1
We present Spatons, a family of compositional, spatially localized neural operators for computationally efficient XR (extended reality) predistortion and distortion compensation. X…
Displacement Is Not Direction: Evaluating Fidelity Metrics for Quantized LLM Deployment
Miloš Nikolić, Ali Hadi Zadeh, Enrique Torres Sanchez +1
Fidelity metrics, such as per-token KL divergence (KLD) against a high-precision reference, are often used in practice as low-cost proxies for benchmark quality. We test this pract…
Low-Bitwidth Floating Point Quantization for Efficient High-Quality Diffusion Models
Cheng Chen, Christina Giannoula, Andreas Moshovos
Diffusion models are emerging models that generate images by iteratively denoising random Gaussian noise using deep neural networks. These models typically exhibit high computation…
Schrödinger's FP: Dynamic Adaptation of Floating-Point Containers for Deep Learning Training
Miloš Nikolić, Enrique Torres Sanchez, Jiahui Wang +5
The transfer of tensors from/to memory during neural network training dominates time and energy. To improve energy efficiency and performance, research has been exploring ways to u…
Mokey: Enabling Narrow Fixed-Point Inference for Out-of-the-Box Floating-Point Transformer Models
Ali Hadi Zadeh, Mostafa Mahmoud, Ameer Abdelhadi +1
Increasingly larger and better Transformer models keep advancing state-of-the-art accuracy and capability for Natural Language Processing applications. These models demand more com…
APack: Off-Chip, Lossless Data Compression for Efficient Deep Learning Inference
Alberto Delmas Lascorz, Mostafa Mahmoud, Andreas Moshovos
Data accesses between on- and off-chip memories account for a large fraction of overall energy consumption during inference with deep learning networks. We present APack, a simple…
FPRaker: A Processing Element For Accelerating Neural Network Training
Omar Mohamed Awad, Mostafa Mahmoud, Isak Edo +5
We present FPRaker, a processing element for composing training accelerators. FPRaker processes several floating-point multiply-accumulation operations concurrently and accumulates…
TensorDash: Exploiting Sparsity to Accelerate Deep Neural Network Training and Inference
Mostafa Mahmoud, Isak Edo, Ali Hadi Zadeh +4
TensorDash is a hardware level technique for enabling data-parallel MAC units to take advantage of sparsity in their input operand streams. When used to compose a hardware accelera…
GOBO: Quantizing Attention-Based NLP Models for Low Latency and Energy Efficient Inference
Ali Hadi Zadeh, Isak Edo, Omar Mohamed Awad +1
Attention-based models have demonstrated remarkable success in various natural language understanding tasks. However, efficient execution remains a challenge for these models which…
BitPruning: Learning Bitlengths for Aggressive and Accurate Quantization
Miloš Nikolić, Ghouthi Boukli Hacene, Ciaran Bannon +5
Neural networks have demonstrably achieved state-of-the art accuracy using low-bitlength integer quantization, yielding both execution time and energy benefits on existing hardware…
Bit-pragmatic Deep Neural Network Computing
J. Albericio, P. Judd, A. Delmás +2
We quantify a source of ineffectual computations when processing the multiplications of the convolutional layers in Deep Neural Networks (DNNs) and propose Pragmatic (PRA), an arch…