10 accepted papers
SRAM-based analog in-memory computing (IMC) architectures have demonstrated high energy efficiency and compute density over digital accelerators for machine learning. However, their compute SNR and achievable dot product (DP) dimension are limited by the analog nature of computations. We present a M…
Analog in-memory computing architectures (IMCs) have exhibited high energy efficiency over conventional digital architectures. The use of resistive memory arrays such as magnetic RAM (MRAM) in IMCs has significant potential due to their high-density and non-volatility. However, the analog nature of…
The demand for the deployment of deep neural networks (DNN) on resource-constrained Edge platforms is ever increasing. Today’s DNN accelerators support mixed-precision computations to enable reduction of computational and storage costs but require networks with precision at variable granularity, i.e…
There has been growing interest in developing neural network based automatic target recognition systems for synthetic aperture radar applications. However, these networks are typically complex in terms of storage and computation which inhibits their deployment in the field, where such resources are…
There has been growing interest in the deployment of deep learning systems onto resource-constrained platforms for fast and efficient inference. However, typical models are overwhelmingly complex, making such integration very challenging and requiring compression mechanisms such as reduced precision…
With the ever growing popularity of deep learning, the tremendous complexity of deep neural networks is becoming problematic when one considers inference on resource constrained platforms. Binary networks have emerged as a potential solution, however, they exhibit a fundamentallimi-tation in realizi…
It is well-known that the precision of data, weight vector, and internal representations employed in learning systems directly impacts their energy, throughput, and latency. The precision requirements for the training algorithm are also important for systems that learn on-the-fly. In this paper, we…
Probabilistic inference is a versatile tool to solve a large variety of pixel-labeling problems in computer vision such as stereo matching and image denoising. Belief Propagation (BP) is an effective method for such inference tasks, and has also shown attractive error-resilience properties—the abili…
This paper presents a novel statistical error compensation (SEC) technique — algorithmic error cancellation (AEC)-for designing robust and energy-efficient signal processing and machine learning kernels on scaled process technologies. AEC exhibits a perfect error compensation (PEC) property, i.e., i…
In this paper, an energy efficient, memory-intensive, and high throughput VLSI architecture is proposed for convolutional networks (C-Net) by employing compute memory (CM) [1], where computation is deeply embedded into the memory (SRAM). Behavioral models incorporating CM's circuit non-idealities an…