Exciting internships at dividiti (deep learning, runtime adaptation, SW/HW co-design)

We wish you a very happy and successful New Year!

If you are passionate about performance analysis and optimization, run-time adaptation and SW/HW co-design, as well as collaborative and reproducible experimentation, we would like to draw your attention to several exciting internships at dividiti available for HiPEAC PhD students:

1. Collective Knowledge on Deep Learning (apply here).
2. Crowdtuning and runtime adaptation of open-source CPU/GPU libraries (apply here).
3. Solving grand challenges in computer systems via knowledge sharing and crowdsourcing (apply here).

You can find general information about HiPEAC internships here. Our internships will be for 3-6 months between February and December 2017 in our fantastic office in Cambridge, UK. Please apply before 1 February 2017!

Collective Knowledge on Deep Learning

You will contribute to our growing suite of open-source tools for crowd-benchmarking and crowd-tuning of deep learning applications (CK-Caffe, CK-TensorFlow, CK-TinyDNN, CK-TensorRT, etc.), being developed in collaboration with our customers and partners.We aim to collectively grow optimisation knowledge on deep learning to meet the performance, prediction accuracy and cost requirements for deployment on a wide range of form factors - from sensors to self-driving cars.

Sounds interesting? Please read more about our initiatives in the latest HiPEAC newsletter (1, 2), try out our Android app and... apply!

Crowdtuning and runtime adaptation of open-source CPU/GPU libraries

Several open-source libraries are readily available (e.g. OpenBLAS, MAGMA, ViennaCL, clBLAS, CLBlast). Unfortunately, in terms of performance they generally trail behind closed-source libraries (e.g. Intel's MKL, NVIDIA's cuBLAS). First, developers typically expose only a few optimization parameters (“knobs”) for tuning, as it’s a very tedious, time-consuming and hardware-specific process. Second, developers have no effective means for optimization knowledge transfer between projects.

You will contribute to an ambitious and exciting open-source initiative to enable library crowd-tuning via our Collective Knowledge framework and repository. This initiative will allow the community to easily compare various implementations of library routines across different data sets and diverse hardware, gradually expose more and more optimization choices, continuously crowd-tune such routines, share optimization statistics in a public repository, and automatically assemble the best and possibly adaptive solution for a given platform.

Sounds interesting? Please read more about our initiatives in the latest HiPEAC newsletter (1, 2), and apply!

Solving grand challenges in computer systems via knowledge sharing and crowdsourcing

You will contribute to solving grand challenges in computer systems research by sharing research artefacts and crowdsourcing experimentation! Please read more about our approach and startup by following the links below and apply!

• Knowledge sharing
• SME focus on dividiti
• 10 mininutes with dividiti