Seyedarmin Azizi

Seyedarmin Azizi, Mahdi Nazemi, Arash Fayyazi et al.

As the complexity and computational demands of deep learning models rise, the need for effective optimization methods for neural network designs becomes paramount. This work introduces an innovative search mechanism for automatically selecting the best bit-width and layer-width for individual neural network layers. This leads to a marked enhancement in deep neural network efficiency. The search domain is strategically reduced by leveraging Hessian-based pruning, ensuring the removal of non-crucial parameters. Subsequently, we detail the development of surrogate models for favorable and unfavorable outcomes by employing a cluster-based tree-structured Parzen estimator. This strategy allows for a streamlined exploration of architectural possibilities and swift pinpointing of top-performing designs. Through rigorous testing on well-known datasets, our method proves its distinct advantage over existing methods. Compared to leading compression strategies, our approach records an impressive 20% decrease in model size without compromising accuracy. Additionally, our method boasts a 12x reduction in search time relative to the best search-focused strategies currently available. As a result, our proposed method represents a leap forward in neural network design optimization, paving the way for quick model design and implementation in settings with limited resources, thereby propelling the potential of scalable deep learning solutions.

Seyedarmin Azizi, Mahdi Nazemi, Massoud Pedram

As Vision Transformers (ViTs) increasingly set new benchmarks in computer vision, their practical deployment on inference engines is often hindered by their significant memory bandwidth and (on-chip) memory footprint requirements. This paper addresses this memory limitation by introducing an activation-aware model compression methodology that uses selective low-rank weight tensor approximations of different layers to reduce the parameter count of ViTs. The key idea is to decompose the weight tensors into a sum of two parameter-efficient tensors while minimizing the error between the product of the input activations with the original weight tensor and the product of the input activations with the approximate tensor sum. This approximation is further refined by adopting an efficient layer-wise error compensation technique that uses the gradient of the layer's output loss. The combination of these techniques achieves excellent results while it avoids being trapped in a shallow local minimum early in the optimization process and strikes a good balance between the model compression and output accuracy. Notably, the presented method significantly reduces the parameter count of DeiT-B by 60% with less than 1% accuracy drop on the ImageNet dataset, overcoming the usual accuracy degradation seen in low-rank approximations. In addition to this, the presented compression technique can compress large DeiT/ViT models to have about the same model size as smaller DeiT/ViT variants while yielding up to 1.8% accuracy gain. These results highlight the efficacy of our approach, presenting a viable solution for embedding ViTs in memory-constrained environments without compromising their performance.

Mohammad Erfan Sadeghi, Arash Fayyazi, Seyedarmin Azizi et al.

The deployment of Vision Transformers (ViTs) on hardware platforms, specially Field-Programmable Gate Arrays (FPGAs), presents many challenges, which are mainly due to the substantial computational and power requirements of their non-linear functions, notably layer normalization, softmax, and Gaussian Error Linear Unit (GELU). These critical functions pose significant obstacles to efficient hardware implementation due to their complex mathematical operations and the inherent resource count and architectural limitations of FPGAs. PEANO-ViT offers a novel approach to streamlining the implementation of the layer normalization layer by introducing a division-free technique that simultaneously approximates the division and square root function. Additionally, PEANO-ViT provides a multi-scale division strategy to eliminate division operations in the softmax layer, aided by a Pade-based approximation for the exponential function. Finally, PEANO-ViT introduces a piece-wise linear approximation for the GELU function, carefully designed to bypass the computationally intensive operations associated with GELU. In our comprehensive evaluations, PEANO-ViT exhibits minimal accuracy degradation (<= 0.5% for DeiT-B) while significantly enhancing power efficiency, achieving improvements of 1.91x, 1.39x, 8.01x for layer normalization, softmax, and GELU, respectively. This improvement is achieved through substantial reductions in DSP, LUT, and register counts for these non-linear operations. Consequently, PEANO-ViT enables efficient deployment of Vision Transformers on resource- and power-constrained FPGA platforms.

Seyedarmin Azizi, Souvik Kundu, Massoud Pedram

Low-rank adaptation (LoRA) has become the default approach to fine-tune large language models (LLMs) due to its significant reduction in trainable parameters. However, trainable parameter demand for LoRA increases with increasing model embedding dimensions, leading to high compute costs. Additionally, its backward updates require storing high-dimensional intermediate activations and optimizer states, demanding high peak GPU memory. In this paper, we introduce large model fine-tuning via spectrally decomposed low-dimensional adaptation (LaMDA), a novel approach to fine-tuning large language models, which leverages low-dimensional adaptation to achieve significant reductions in trainable parameters and peak GPU memory footprint. LaMDA freezes a first projection matrix (PMA) in the adaptation path while introducing a low-dimensional trainable square matrix, resulting in substantial reductions in trainable parameters and peak GPU memory usage. LaMDA gradually freezes a second projection matrix (PMB) during the early fine-tuning stages, reducing the compute cost associated with weight updates to enhance parameter efficiency further. We also present an enhancement, LaMDA++, incorporating a ``lite-weight" adaptive rank allocation for the LoRA path via normalized spectrum analysis of pre-trained model weights. We evaluate LaMDA/LaMDA++ across various tasks, including natural language understanding with the GLUE benchmark, text summarization, natural language generation, and complex reasoning on different LLMs. Results show that LaMDA matches or surpasses the performance of existing alternatives while requiring up to 17.7x fewer parameter updates and up to 1.32x lower peak GPU memory usage during fine-tuning. Code will be publicly available.

Seyedarmin Azizi, Mahdi Nazemi, Mehdi Kamal et al.

This paper presents a mixed-computation neural network processing approach for edge applications that incorporates low-precision (low-width) Posit and low-precision fixed point (FixP) number systems. This mixed-computation approach employs 4-bit Posit (Posit4), which has higher precision around zero, for representing weights with high sensitivity, while it uses 4-bit FixP (FixP4) for representing other weights. A heuristic for analyzing the importance and the quantization error of the weights is presented to assign the proper number system to different weights. Additionally, a gradient approximation for Posit representation is introduced to improve the quality of weight updates in the backpropagation process. Due to the high energy consumption of the fully Posit-based computations, neural network operations are carried out in FixP or Posit/FixP. An efficient hardware implementation of a MAC operation with a first Posit operand and FixP for a second operand and accumulator is presented. The efficacy of the proposed low-precision mixed-computation approach is extensively assessed on vision and language models. The results show that, on average, the accuracy of the mixed-computation is about 1.5% higher than that of FixP with a cost of 0.19% energy overhead.

Jung Hwan Heo, Seyedarmin Azizi, Arash Fayyazi et al.

Transfer learning has become a popular task adaptation method in the era of foundation models. However, many foundation models require large storage and computing resources, which makes off-the-shelf deployment impractical. Post-training compression techniques such as pruning and quantization can help lower deployment costs. Unfortunately, the resulting performance degradation limits the usability and benefits of such techniques. To close this performance gap, we propose CrAFT, a simple fine-tuning framework that enables effective post-training network compression. In CrAFT, users simply employ the default fine-tuning schedule along with sharpness minimization objective, simultaneously facilitating task adaptation and compression-friendliness. Contrary to the conventional sharpness minimization techniques, which are applied during pretraining, the CrAFT approach adds negligible training overhead as fine-tuning is done in under a couple of minutes or hours with a single GPU. The effectiveness of CrAFT, which is a general-purpose tool that can significantly boost one-shot pruning and post-training quantization, is demonstrated on both convolution-based and attention-based vision foundation models on a variety of target tasks. The code will be made publicly available.