CENNA: Cost-Effective Neural Network Accelerator

A neural network accelerator for image analysis

The Connex Arraytmas a Neural Network Accelerator

We discuss the parallel implementation of neural networks on a Connex ArrayTMcircuit. By estimating the number of memory cycles, we approximate the real performance of the machine. We show how to implement a basic neural learning algorithm. Our preliminary study suggests that the Connex ArrayTMis a good neural network accelerator, due to its high performance for vector operations. The execution...

Cost-effective VPC network design

Complex environments for ATM based telecommunication services assume high availability and reliability of the network resources as well as the services. In order to reduce the cost of building and maintaining wide area ATM networks, efficient and cost-effective resource management techniques are needed to avoid over-allocation of resources. This requires building a network having the intelligen...

Applying Data Compression Techniques on Systolic Neural Network Accelerator

New directions in computing and algorithms has lead to some new applications that have tolerance to imprecision. Although, These applications are creating large volumes of data which exceeds the capability of today’s computing systems. Therefore, researchers are trying to find new techniques to alleviate this crisis. Approximate Computing is one promising technique that uses a trade off between...

A Large-Scale Spiking Neural Network Accelerator for FPGA Systems

Spiking neural networks (SNN) aim to mimic membrane potential dynamics of biological neurons. They have been used widely in neuromorphic applications and neuroscience modeling studies. We design a parallel SNN accelerator for producing large-scale cortical simulation targeting an off-theshelf Field-Programmable Gate Array (FPGA)-based system. The accelerator parallelizes synaptic processing wit...