ESWEEK

Convolutional neural networks are computationally intensive. A single inference can require billions of multiply/accumulate operations. In the datacenter, where ample power, space, and cooling are available, high powered CPUs or GPUs can be used. However,...

Approximate circuits sacrifice accuracy in exchange for energy efficiency and have been widely used in hardware deployment of neural networks (NNs). Since convolution accounts for most of the power consumption in NNs, it is necessary to design an ...

Dropping the features/kernels in the convolutional layer of convolutional neural networks is a popular variant of structured pruning to reduce the computational load, but this comes at the cost of retraining and performance loss. In this work, we propose ...

We present an infrastructure for evaluating CNN models for vulnerability against a variety of integrity attacks. Our focus is on attacks that corrupt CNN computations with an impact on prediction/classification accuracy. The attack model encompasses a ...

Emerging Artificial-Intelligence-enabled System-on-Chips (AI-SoCs) combine a flexible microcontroller with parallel Digital Signal Processors (DSP) and heterogeneous acceleration capabilities. In this Work-in-Progress paper, we focus on the GAP9 RISC-V ...

This paper explores the challenges and opportunities of integrating non-volatile memories (NVMs) into embedded systems for machine learning. NVMs offer advantages such as increased memory density, lower power consumption, non-volatility, and compute-in-...