Search Results for author:
Found 21 papers, 7 papers with code
SubgoalXL: Subgoal-based Expert Learning for Theorem Proving
This paper introduces SubgoalXL, a novel approach that synergizes subgoal-based proofs with expert learning to enhance LLMs' capabilities in formal theorem proving within the Isabelle environment.
Constructing Domain-Specific Evaluation Sets for LLM-as-a-judge
The agreement values are 9\% better than Arena Hard and 20\% better than AlpacaEval 2. 0 LC, while the Spearman coefficient is 0. 7 more than the next best benchmark, showcasing a significant improvement in the usefulness of the benchmark.
SambaNova SN40L: Scaling the AI Memory Wall with Dataflow and Composition of Experts
In this paper, we describe how combining CoE, streaming dataflow, and a three-tier memory system scales the AI memory wall.
SambaLingo: Teaching Large Language Models New Languages
In this paper, we present a comprehensive investigation into the adaptation of LLMs to new languages.
Efficiently Adapting Pretrained Language Models To New Languages
Recent large language models (LLM) exhibit sub-optimal performance on low-resource languages, as the training data of these models is usually dominated by English and other high-resource languages.
Training Large Language Models Efficiently with Sparsity and Dataflow
We show that we can successfully train GPT 13B to the same quality as the dense GPT 13B model, while achieving an end-end speedup of 4. 5x over dense A100 baseline.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts
PromptSource is a system for creating, sharing, and using natural language prompts.
Multitask Prompted Training Enables Zero-Shot Task Generalization
Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020).
MLPerf Tiny Benchmark
Advancements in ultra-low-power tiny machine learning (TinyML) systems promise to unlock an entirely new class of smart applications.
Doping: A technique for efficient compression of LSTM models using sparse structured additive matrices
Additionally, results with doped kronecker product matrices demonstrate state-of-the-art accuracy at large compression factors (10 - 25x) across 4 natural language processing applications with minor loss in accuracy.
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers
To address this challenge, neural architecture search (NAS) promises to help design accurate ML models that meet the tight MCU memory, latency and energy constraints.
Ranked #1 on
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on Google Speech Commands V2 12
Rank and run-time aware compression of NLP Applications
We evaluate the impact of this technique on 5 NLP benchmarks across multiple tasks (Translation, Intent Detection, Language Modeling) and show that for similar accuracy values and compression factors, HMF can achieve more than 2. 32x faster inference run-time than pruning and 16. 77% better accuracy than LMF.
Benchmarking TinyML Systems: Challenges and Direction
In this position paper, we present the current landscape of TinyML and discuss the challenges and direction towards developing a fair and useful hardware benchmark for TinyML workloads.
Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art
Nowadays, devices are equipped with advanced sensors with higher processing/computing capabilities.
Compressing Language Models using Doped Kronecker Products
Kronecker Products (KP) have been used to compress IoT RNN Applications by 15-38x compression factors, achieving better results than traditional compression methods.
Ternary MobileNets via Per-Layer Hybrid Filter Banks
Using this proposed quantization method, we quantized a substantial portion of weight filters of MobileNets to ternary values resulting in 27. 98% savings in energy, and a 51. 07% reduction in the model size, while achieving comparable accuracy and no degradation in throughput on specialized hardware in comparison to the baseline full-precision MobileNets.
Pushing the limits of RNN Compression
This paper introduces a method to compress RNNs for resource constrained environments using Kronecker product (KP).
A Static Analysis-based Cross-Architecture Performance Prediction Using Machine Learning
Porting code from CPU to GPU is costly and time-consuming; Unless much time is invested in development and optimization, it is not obvious, a priori, how much speed-up is achievable or how much room is left for improvement.
Run-Time Efficient RNN Compression for Inference on Edge Devices
Recurrent neural networks can be large and compute-intensive, yet many applications that benefit from RNNs run on small devices with very limited compute and storage capabilities while still having run-time constraints.
Compressing RNNs for IoT devices by 15-38x using Kronecker Products
Recurrent Neural Networks (RNN) can be difficult to deploy on resource constrained devices due to their size. As a result, there is a need for compression techniques that can significantly compress RNNs without negatively impacting task accuracy.
