80 citations · 126 across the 5 of their papers we have counts for
6 papers
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…
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…
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…