Acceleration and Transprecision

A NVIDIA Tesla V100 GP-GPU. This cutting-edge accelerator provides huge computational power on a massive 800 mm² die.

Google's Cloud TPU (Tensor Processing Unit). This machine learning accelerator can do one thing extremely well: multiply-accumulate operations.

Accelerators are the backbone of big data and scientific computing. While general purpose processor architectures such as Intel's x86 provide good performance across a wide variety of applications, it is only since the advent of general purpose GPUs that many computationally demanding tasks have become feasible. Since these GPUs support a much narrower set of operations, it is easier to optimize the architecture to make them more efficient. Such accelerators are not limited to high performance sector alone. In low power computing, they allow complex tasks such as computer vision or cryptography to be performed under a very tight power budget. Without a dedicated accelerator, these tasks would not be feasible.

Who We Are

Francesco Conti

• fconti@iis.ee.ethz.ch
• ETZ J78

Stefan Mach

• smach@iis.ee.ethz.ch
• ETZ J89

Fabian Schuiki

• fschuiki@iis.ee.ethz.ch
• ETZ J89

Manuel Eggimann

• meggiman@iis.ee.ethz.ch
• ETZ J68

Luca Bertaccini

• lbertaccini@iis.ee.ethz.ch
• ETZ J78

Matteo Perotti

• mperotti@iis.ee.ethz.ch
• ETZ J85

Available Projects

• RVfplib
• Scalable Heterogeneous L1 Memory Interconnect for Smart Accelerator Coupling in Ultra-Low Power Multicores

Projects In Progress

• An all Standard-Cell Based Energy Efficient HW Accelerator for DSP and Deep Learning Applications

Completed Projects

The Logarithmic Number Unit chip Selene.

• NVDLA meets PULP
• Hardware Accelerators for Lossless Quantized Deep Neural Networks
• Floating-Point Divide & Square Root Unit for Transprecision
• Low-Energy Cluster-Coupled Vector Coprocessor for Special-Purpose PULP Acceleration
• Design and Implementation of an Approximate Floating Point Unit