20 citations · 29 across the 17 of their papers we have counts for
17 papers
The Art of Closed-Formula Defaults: Search-Free Code Generation for Tensor Operators
Paolo D'Alberto, Ashish Sirasao
Agentic search and automated optimization of GPU kernels are powerful tools for large language model inference. Their effectiveness, however, depends not on the sophistication of t…
Lindblad Multiproduct Formulas
Niall F. Robertson, Andrea D'Urbano, Anton Dekusar +11
We introduce Lindblad Multiproduct Formulas: a quantum error mitigation technique that uses two-dimensional tensor networks contracted with loop-corrected belief propagation. The q…
The Pauli Lightcone: Information-Theoretic Error Mitigation Beyond the Autocorrelation
Paolo D'Alberto
We introduce the wavemap: a spatial portrait of noise effects that assigns each site a per-noise-level arrival delay l_γ(v) and cross-entropy loss L_γ(v). These observables are exa…
Tensor Network Simulation of the Heisenberg Model on Heavy-Hex Lattices
Paolo D'Alberto
We extend CppSim, a high-performance C++/HIP tensor network simulator, to the heavy-hex lattice geometry used in superconducting quantum processors. The heavy-hex graph -- a bipart…
GPU-First Heisenberg-Picture Tensor Network Dynamics for the 2D Transverse-Field Ising Model
Paolo D'Alberto
We present CppSim, a C++/GPU 2D Ising simulator for Heisenberg-picture tensor network time evolution on GPUs. The key computational contributions are: first, a zero-malloc GPU work…
Ablation, Statistical Inference, and Validation for KV-Cache Compression
Paolo D'Alberto, Ashish Siarasao, Elliott Delaye +1
This study systematically compares Turbo-Quant and SpectralQuant KV-cache compression, evaluating non-dominated schemes, including WHT rotation with Beta Lloyd-Max and QJL, through…
Statistical Inference and Quality Measures of KV Cache Quantisations Inspired by TurboQuant
Paolo D'Alberto
We analyse three KV cache quantization schemes under a fair bit budget: \textbf{KV} (scalar MSE baseline), \textbf{KQV} (WHT + MSE on ; WHT + MSE + QJL on ), and \textbf{QKQV…
Weight Block Sparsity: Training, Compilation, and AI Engine Accelerators
Paolo D'Alberto, Taehee Jeong, Akshai Jain +5
Nowadays, increasingly larger Deep Neural Networks (DNNs) are being developed, trained, and utilized. These networks require significant computational resources, putting a strain o…
Strassen's Matrix Multiplication Algorithm Is Still Faster
Paolo D'Alberto
Recently, reinforcement algorithms discovered new algorithms that really jump-started a wave of excitements and a flourishing of publications. However, there is little on implement…
Entropy Maximization in Sparse Matrix by Vector Multiplication ()
Paolo D'Alberto, Abhishek Jain, Ismail Bustany +2
The peak performance of any SpMV depends primarily on the available memory bandwidth and its effective use. GPUs, ASICs, and new FPGAs have higher and higher bandwidth; however, fo…
Digital Advertising: the Measure of Mobile Visits Lifts
Paolo D'Alberto, Veronica Milenkiy, Fairiz Fi Azizi
Mobile-phone advertising enables marketers to reach customers at a personal level and it enables the measure of costumers reaction by novel approaches, in real time, and at scale.…
DPUV3INT8: A Compiler View to programmable FPGA Inference Engines
Paolo D'Alberto, Jiangsha Ma, Jintao Li +3
We have a FPGA design, we make it fast, efficient, and tested for a few important examples. Now we must infer a general solution to deploy in the data center. Here, we describe the…
Quantizing Convolutional Neural Networks for Low-Power High-Throughput Inference Engines
Sean O. Settle, Manasa Bollavaram, Paolo D'Alberto +6
Deep learning as a means to inferencing has proliferated thanks to its versatility and ability to approach or exceed human-level accuracy. These computational models have seemingly…
Mapping and Matching Algorithms: Data Mining by Adaptive Graphs
Paolo D'Alberto, Veronica Milenkly
Assume we have two bijective functions and with for all and . Every day and in different locations, we see the different…
Multiple-Campaign Ad-Targeting Deployment: Parallel Response Modeling, Calibration and Scoring Without Personal User Information
Paolo D'Alberto
We present a vertical introduction to campaign optimization; that is, the ability to predict the user response to an ad campaign without any users' profiles on average and for each…
Non-Parametric Methods Applied to the N-Sample Series Comparison
Paolo D'Alberto, Chris Drome, Ali Dasdan
Anomaly and similarity detection in multidimensional series have a long history and have found practical usage in many different fields such as medicine, networks, and finance. Ano…
On the Weakenesses of Correlation Measures used for Search Engines' Results (Unsupervised Comparison of Search Engine Rankings)
Paolo D'Alberto, Ali Dasdan
The correlation of the result lists provided by search engines is fundamental and it has deep and multidisciplinary ramifications. Here, we present automatic and unsupervised metho…