论文代码 AAAI2024论文合集: 中文关键词 论文标题 Memory-Efficient Reversible Spiking Neural Networks 内存高效可逆脉冲神经网络 论文链接 Memory-Efficient Reversible Spiking Neural Networks论文下载 论文作者 Hong Zhang, Yu Zhang 内容简介 本文提出了一种可逆脉冲神经网络(RevSNN),旨在降低脉冲神经网络(SNNs)在...
TL, DR: 提出了Gradient Low-Rank Projection(GaLore)方法,以更memory-efficient的方式来实现全参数微调学习。(allows full parameter learning but is more memory-efficient than common low-rank adaptation methods such as LoRA.) 简单来说,讲 lora 的low-rank 思路迁移到 LLM 的训练之中,不是对权重 W 做 ...
在单个强化学习任务中,遗忘问题未得到充分探索和解决,因为该问题被使用大型回放缓冲区掩盖了。本文旨在开发 memory-efficient的单任务强化学习算法,同时通过减少灾难性遗忘实现高样本效率和训练性能。 4 MeDQN: Memory-efficient DQN 4.1 RL Background 4.2 Knowledge consolidation 最初,Hinton等人(2014...
投影梯度下降(Projected Gradient Descent, PGD):GaLore与传统的PGD相关,但GaLore考虑了在训练多层神经网络时自然出现的特定梯度形式,并证明了其许多属性。 内存高效的优化(Memory-Efficient Optimization):一些工作尝试减少自适应优化算法的梯度统计的内存成本。例如,Adafactor通过分解二阶统计量来实现亚线性内存成本。 量化(...
To materialize HPCache, we provide a model that captures per-input caching impact (Sect.4.1) and use it to provide a memory-efficient caching configuration (Sect.4.2). Finally, we show how HPCache continuously adapts its caching configuration based on updated estimates and caching configurations ...
This paper focuses on improving the memory efficiency ofinterpretation without sacrificing precision or time efficiency. Computationally,interpretation reduces the problem of inferring program invariants to computing a fixpoint of a set of equations. This paper presents a method to minimize the memory ...
本文主要是Pytorch2.0 的小实验,在MacBookPro 上体验一下等优化改进后的Transformer Self Attention的性能,具体的有 FlashAttention、Memory-Efficient Attention、CausalSelfAttention 等。主要是torch.compile(model) 和 scaled_dot_product_attention的使用。
Code MEGAHIT MEGAHIT is an ultra-fast and memory-efficient NGS assembler. It is optimized for metagenomes, but also works well on generic single genome assembly (small or mammalian size) and single-cell assembly. Installation Conda conda install -c bioconda megahit ...
A high-throughput and memory-efficient inference and serving engine for LLMs docs.vllm.ai Topics amd cuda inference pytorch transformer llama gpt rocm model-serving tpu hpu mlops xpu llm inferentia llmops llm-serving qwen deepseek trainium Resources Readme License Apache-2.0 license Code...
Code completion is one of the most widely used features of modern integrated development environments (IDEs). Deep learning has recently made significant progress in the statistical prediction of source code. However, state-of-the-art neural network models consume prohibitively large amounts...