EE380 Computer Systems Colloquium

During this session, the speakers will provide an overview of Wave Computing's MIPS Open initiative, including details on the program components, how they can be used to design edge SoCs, licensing terms, the design certification process, etc. Mr. Bemanian will also give a demonstration of how to use various program components for real-world example implementations.

Wave Computing released the first MIPS Open program components at the end of March, providing free access to the MIPS RISC architecture without license fees or royalties. The new MIPS Open online environment is live and immediately accessible at www.mipsopen.com. Specific components in the first program release include:

• MIPS Instruction Set Architecture: A downloadable copy of the latest version of the MIPS 32/64-bit ISA, SIMD, DSP, Multithreading and Virtualization
• MIPS Open Toolsi: Integrated Development Environment for embedded real-time operating systems and Linux-based systems for embedded products
• MIPS Open FPGAs: A complete training program including labs, SoC tutorials and sample (non-commercial) RTL code
• MIPS Open Cores: low power, low footprint microAptiv Microprocessor(MPU) and Microcontroller (MCU) cores targeted for embedded applications

Historical Note The MIPS Architecture and processor was originally developed in the Computer Systems Laboratory at Stanford by a team headed by John Hennessey. MIPS and the UC Berkeley developed SPARC archicture were quintessential RISC architectures: influential, popular, and heavily studied.

Individuals of all genders invited to be a part of:
#StanfordToo: A Conversation about Sexual Harassment in Our Academic Spaces, where we will feature real stories of harassment at Stanford academic STEM in a conversation with Provost Drell, Dean Minor (SoM), and Dean Graham (SE3). We will have plenty of time for audience discussion on how we can take concrete action to dismantle this culture and actively work towards a more inclusive Stanford for everyone. While our emphasis is on STEM fields, we welcome and encourage participation from students, postdocs, staff, and faculty of all academic disciplines and backgrounds.

Although FPGAs continue to grow in capacity, FPGA-based soft processors have grown little because of the difficulty of achieving higher performance in exchange for area. Superscalar out-of-order processor microarchitectures have been used successfully for hard processors for many years, but have so far been avoided for FPGAs due to the area increase and the expectation that a loss in clock frequency would more than offset the instructions-per-cycle (IPC) gains.

This talk summarizes my attempt at designing an out-of-order x86 CPU for FPGA. With careful microarchitectural choices and circuit design, I show that it is possible to build a complex microarchitecture on an FPGA, getting about 2.7x performance per clock and 0.8x clock frequency of Altera's Nios II/f single-issue in-order processor. This talk will cover a high-level overview of the microarchitecture and some of the interesting LUT-based circuits used in the processor.

Wirelessly-enabled sensing technologies offer the hope of enabling economically and socially important applications including intelligent transportation systems and other so-called smart city initiatives, home automation, improvements in manufacturing, and tools for humanitarian assistance and disaster response. Importantly, the value may only be realizable through the integration of a number of dis-similar, separately-built, separately-owned, and separately-controlled sensing sub-systems. SMILE -- the Synchronized Multi-sensory Integrated Learning Environment -- is a CMU project that aims to create, deploy and validate a framework that addresses key challenges that arise in such a system-of-systems.

In this talk, we present early results of the SMILE project. We begin with an examination of some practical barriers in a typical smart city application. From this, we develop themes related to low-power operation, programmability, synchronization, federation, and the interplay between them. Within this context, we discuss four sub-projects: (1) a synchronization approach for low-power devices, (2) a family of visual sensing techniques that explore the domain of real-time learning onboard small unmanned aerial systems (sUAS), (3) a demonstration system that combines information from such sUAS with intelligent ground-based sensors, and (4) the sketch of a new language framework, called TickTalk, that seeks to ease the programmer's burden in creating software for such complex, heterogeneous, distributed, fault-prone systems.

Fingerprint research seeks to improve understanding of the nature and causes of climate change. The basic strategy is to search for model-predicted patterns of climate change ("fingerprints") in observed climate records. Such studies exploit the fact that different factors affecting climate have different characteristic signatures. These unique attributes are clearer in detailed patterns of climate change than in global-mean climate information. Fingerprinting is a powerful tool for separating human and natural climate-change signals. Results from fingerprint research provide scientific support for findings of a "discernible human influence" on global climate.

Twenty-four years ago, at the time of publication of the Second Assessment Report of the Intergovernmental Panel on Climate Change, most fingerprint studies relied on surface temperature. Critics of this work argued that a human-caused fingerprint should be identifiable in many different aspects of the climate system, and not in surface thermometer records alone. Climate scientists responded to this justifiable criticism by moving beyond early "temperature only" ingerprint studies, interrogating modeled and observed changes in rainfall, water vapor, river runoff, snowpack depth, atmospheric circulation, salinity, and many other climate variables. The message of this body of work is that human-caused fingerprints are ubiquitous in the climate system.

My lecture looks back at over two decades of efforts to identify human effects on global climate. It will also address some personal lessons learned since publication of the "discernible human influence finding".

Jupyter Notebooks are a emerging way to express and present research results. Notebooks are a kind of IDE Framework for doing science, one which is better suited to presenting algorithms and data than the ad hoc frameworks currently used.

The creator of Jupyter Notebooks (Fernando) and the creator of the Pythons language (Guido) will help you understand why you should use Jupyter Notebooks for your next paper.

VVateR is a virtual three-dimensional world for visualizing Cyber-Physical Systems such as a city-wide water treatment and water distribution plant. A key novelty of VVateR is its ability to enable visualization of cyber- attacks, the resulting process anomalies, and whether or not the anomaly is detected. VVateR is currently operational in iTrust. It is connected to two plants, namely a water treatment plant named SWaT, and a water distribution plant named WADI. Both SWaT and WADI are fully operational plants. VVateR is accessed by wearing a virtual reality headset where the user/gamer can move about and interact with the plant in the virtual space. This opens up the plants for remote worldwide research collaboration and aids in capturing context from the plant that Mixed Reality promises to bring to industrial settings. VVateR helps visualise the interconnectedness of various infrastructures and the effects of cyber-physical attacks through complex and dangerous scenarios that can be safely tested in a virtual setting. Observing slow historical plant operation and path of attacks at varying timelapse rates makes the process of reconnaissance and incident-analysis arguably faster and more visually engaging than an analysis of the database logs. By acting as a Digital Twin when connected to a simulator, one can come up with numerous attack/defense scenarios and serious gamified challenges for training purposes. All these factors increase the preparedness of operators, policymakers, governments, and other relevant stakeholders in strengthening their cities and Critical Infrastructures through security by design. Other relevant projects being done at iTrust will also be discussed in this talk.

details TBA

Computers connected by networks have long used protocols like Network Time Protocol (NTP) to adjust the clocks of the computers to show the same time of day. With machines connected by a high performance network in a data center, NTP can keep the clocks within a few hundred microseconds of each other, a clock skew that is fine for humans but not useful for distributed system software that can communicate between machines in a few tens of microseconds. In this talk I will describe the challenge of tightly synchronizing clocks in a data center and present Huygens, a new algorithm developed with my colleague Balaji Prabhakar's research group that can synchronized data center clocks within few 10s of nanoseconds. Like NTP, Huygens requires no special hardware and simply exchanges messages between machines but uses a number of techniques that exploit the high-performance networks in data centers. The techniques include sending many more probe messages and then using sophisticated machine learning techniques and global knowledge to compute the clock adjustment operations to keep the clock synchronized.

The use of algorithms in clinical care demands a very high performance level for accurate detection and classification of disease. Deep learning (DL) offers a powerful toolkit necessary to handle the complex variations present in medical data, which traditional statistical or machine learning approaches have historically been unable to capture. In this talk, I will describe the challenges and approaches for the development of high-performance DL algorithms and curation of datasets for problems in diagnostic radiology and cardiology. I will also discuss the use of these algorithms as diagnostic support tools for clinicians, and challenges for the potential translation of these algorithms from the lab setting to clinical practice.