Giovanni De Micheli

Chang Meng, Hanyu Wang, Yuyang Ye et al.

Reducing power consumption in AI accelerators is increasingly important. Approximate computing can reduce power consumption while keeping the accuracy loss small. Since multipliers are power-hungry components in AI models, this paper focuses on synthesizing low-power approximate multipliers (AxMs). Unlike prior works that design AxMs separately from AI model training, we present TRAM, which jointly optimizes the AxM structure and AI model parameters to lower power with small accuracy loss. Experiments show that compared to state-of-the-art AxMs, TRAM achieves up to 25.05% AxM power reduction on CNNs with CIFAR-10, and reduces power by up to 27.09% on vision transformers with ImageNet.

Chang Meng, Wayne Burleson, Giovanni De Micheli

Approximate multipliers (AppMults) are widely used in deep learning accelerators to reduce their area, delay, and power consumption. However, AppMults introduce arithmetic errors into deep learning models, necessitating a retraining process to recover accuracy. A key step in retraining is computing the gradient of the AppMult, i.e., the partial derivative of the approximate product with respect to each input operand. Existing approaches typically estimate this gradient using that of the accurate multiplier (AccMult), which can lead to suboptimal retraining results. To address this, we propose two methods to obtain more precise gradients of AppMults. The first, called LUT-2D, characterizes the AppMult gradient with 2-dimensional lookup tables (LUTs), providing fine-grained estimation and achieving the highest retraining accuracy. The second, called LUT-1D, is a compact and more efficient variant that stores gradient values in 1-dimensional LUTs, achieving comparable retraining accuracy with shorter runtime. Experimental results show that on CIFAR-10 with convolutional neural networks, our LUT-2D and LUT-1D methods improve retraining accuracy by 3.83% and 3.72% on average, respectively. On ImageNet with vision transformer models, our LUT-1D method improves retraining accuracy by 23.69% on average, compared to a state-of-the-art retraining framework.

Samuel Bosch, Alexander Sanchez de la Cerda, Mohsen Imani et al.

Machine Learning algorithms based on Brain-inspired Hyperdimensional(HD) computing imitate cognition by exploiting statistical properties of high-dimensional vector spaces. It is a promising solution for achieving high energy efficiency in different machine learning tasks, such as classification, semi-supervised learning, and clustering. A weakness of existing HD computing-based ML algorithms is the fact that they have to be binarized to achieve very high energy efficiency. At the same time, binarized models reach lower classification accuracies. To solve the problem of the trade-off between energy efficiency and classification accuracy, we propose the QubitHD algorithm. It stochastically binarizes HD-based algorithms, while maintaining comparable classification accuracies to their non-binarized counterparts. The FPGA implementation of QubitHD provides a 65% improvement in terms of energy efficiency, and a 95% improvement in terms of training time, as compared to state-of-the-art HD-based ML algorithms. It also outperforms state-of-the-art low-cost classifiers (such as Binarized Neural Networks) in terms of speed and energy efficiency by an order of magnitude during training and inference.

Cunxi Yu, Houping Xiao, Giovanni De Micheli

Design flows are the explicit combinations of design transformations, primarily involved in synthesis, placement and routing processes, to accomplish the design of Integrated Circuits (ICs) and System-on-Chip (SoC). Mostly, the flows are developed based on the knowledge of the experts. However, due to the large search space of design flows and the increasing design complexity, developing Intellectual Property (IP)-specific synthesis flows providing high Quality of Result (QoR) is extremely challenging. This work presents a fully autonomous framework that artificially produces design-specific synthesis flows without human guidance and baseline flows, using Convolutional Neural Network (CNN). The demonstrations are made by successfully designing logic synthesis flows of three large scaled designs.