Functional verification of RTL is one of the primary and most time consuming tasks of microprocessor design. However, designs cannot be completely verified due to their large size and strict time-to-market restrictions. Being more scalable, simulation-based verification has become the mainstay of functional verification. A majority of the randomly generated tests used for design simulation are, however, redundant. This work proposes a two-step approach to achieve faster verification closure.
We propose a novel methodology to improve the effectiveness of one test generator with respect to another. First, we evaluate the effectiveness of using a legacy test generator used at AMD and quantify its verification performance. We explore the differences in the design and capabilities between the legacy test generator and AMD’s latest in-house x86 ISA-based test generator. We then proceed to gather experimental evidence to support our understanding. We propose to utilize external test filters to overcome the limitations of the latest exerciser.
We develop a test filtration approach that is independent of the test generator, to filter out ineffective tests prior to RTL simulation. We achieve this by using ISA simulation traces. We find that using a combination of ISA simulation traces and microarchitectural models is necessary to cover a wider range of coverpoints. Our work shows that by using implementation specific details for extrapolating test behavior information present in simulation traces, we can compensate for microarchitecture agnostic test generation and consequently improve the effectiveness of a test generator without modifying its design. The proposed approach expedites coverage closure by providing precise control over random test behavior. Experimental results based on the latest AMD multi-core processor design are presented to demonstrate the feasibility of our proposed approach.
Eight researchers from the UCSB College of Engineering included in the 2014 Thomson Reuters report on the the most “Highly Cited researchers” in the world
Highly Cited Researchers 2014 represents some of world’s leading scientific minds. Over three thousand researchers earned the distinction by writing the greatest numbers of reports officially designated by Essential Science Indicators as Highly Cited Papers – ranking among the top 1% most cited for their subject field and year of publication, earning them the mark of exceptional impact.
UCSB College of Engineering researchers include:
- Professor Umesh Mishra (ECE)
- Professor Art Gossard (ECE & Materials)
- Professor Craig Hawker (Materials and Chemistry & Biochemistry)
- Professor Alan Heeger (Physics and Materials)
- Professor Guillermo Bazan (Chemistry & Biochemistry and Materials)
- Professor Galen Stucky (Chemistry & Biochemistry and Materials)
- Professor Chris Van De Walle (Materials)
- Daniel Moses (CPOS)
U.S. News & World Report includes UC Santa Barbara in its annual listing of the “Top 30 Public National Universities” in the country, as well on its list of the “Best National Universities.” UCSB’s College of Engineering undergraduate program is also included in their list of “Best Programs at Engineering Schools Whose Highest Degree is a Doctorate.”
According to U.S. News & World Report, UCSB — which this year experienced the most competitive admissions process in campus history — has jumped to number 10 among the “Top 30 Public National Universities.” Among national universities, including both public and private institutions, UCSB moved up to number 40. The campus tied with Lehigh University.
In addition, the undergraduate program in UCSB’s College of Engineering is ranked number 36 on the U.S. News & World Report list of “Best Programs at Engineering Schools Whose Highest Degree is a Doctorate.” Among engineering schools at public universities, UCSB’s College of Engineering placed at number 20. UCSB is tied with the University of Colorado-Boulder, the University of Florida and the University of Notre Dame.
The race to fulfill Moore’s Law has lead to billion-scale transistor density, enabling ubiquitous and computationally diverse architectures. These device technologies have shaped the system-on-a-chip (SoC) landscape, opening new design opportunities in field-programmable gate arrays (FPGAs), hybrid FGPAs, full custom integrated chips, and commercial fixed-architecture platforms. The common thread across these platforms is the integration of distributed memories (including cached hierarchies), computational pipelines, and communication ports/networks. Making efficient use of these components, however, is an enormous challenge. Automated cosynthesis addresses the challenge through optimized partitioning, scheduling, mapping, and binding of an application onto heterogeneous systems.
This research introduces the hierarchical transaction model, a formal, abstract model of computation designed to enable synthesis across the varying semantic and execution models pervasive in heterogeneous systems. This novel specification-to-silicon methodology copes with changing architectures, enabling structured cosynthesis that is resilient to shifting constraints. High-level application behavior is codified through a carefully constructed semantic model ensuring robust design methodology, verifiable concurrent models, and maximal exploitation of parallelism. It further leverages a unique data/execution hierarchical encapsulation framework to guarantee scalable analysis.
At the front end, the model is represented with a practical, understandable specification language that encourages concise design of complex applications. The language focuses designer intent through specific limitations in how state is accessed. The benefit of transactional interactions empowers designers with tools to directly identify and address concurrency bugs. The language is structured with synthesis in mind — it provides methods of expression that are confined to actions commensurate with optimization. Designers express families of valid executions in a minimal format through high-level dependencies, type systems, and computational relationships.
This defense introduces the Hierarchical Transactional Language, its underlying semantic model, along with its compiler, simulator, and studies into high performance synthesis. Everyone is welcome!
Interfacing with the brain is a challenging problem. While many innovative methods for providing input and output to neural systems have been developed and demonstrated successfully in human patients, these invasive systems use less biologically compatible means than are realizable. Materials and mechanisms which are closer mimics to biological systems in their behaviors can lead to more stable and effective medical prosthetic and research devices.
Retinal prosthetics as well as Cochlear implants, are neural implants which provide stimulation via electrical impulses. Currents passing across neurons trigger neurons to begin firing or generating an action potential down their axons, stimulating neurons with dendrites connected to those axons terminals. This scheme transduces the desired simulation into neural firing spikes, but the majority of applied current is shunted around neurons rather than contributing to stimulation. Excess current contributes to the power and thermal budgets of neural simulation devices, which are implanted in tissue, limiting their functionality. Additionally, the electrical contacts which provide a source and return for the stimulation currents are subject to degradation over time, as currents are repeatedly applied during stimulation events. Stimulation of neurons via local delivery of potassium in excess of available intercellular potassium can be used in place of direct electrical stimulation and promises to be a more biologically compatible method.
An orthogonal issue is neural recording. Several devices have been developed and the widest know and highest density is the Utah array, a 3D array of silicon spires, which can record from their tips when inserted into neural tissue. While this 3D array topology can access a field of neural activity, the stiffness of these silicon spires is very different than that of neural tissue, which can lead to an unwanted inflammatory response. Conductive polymer pillars made of softer materials that are a closer match to neural tissues, while mirroring the Utah array’s density and insertion length, may provide a better recording mechanism due to their improved mechanical compatibility with neural tissues.
This thesis investigates microfabrication schemes to produce biomimetic structures that can enable neural simulation and recording devices which feature greater biological stability and improve utility.
The importance of synchronization in biological and engineering systems has triggered an avalanche of studies analyzing the emergence of a synchronized behavior within a network of, possibly heterogeneous, agents. In particular, synchronization of networks of coupled oscillators has received great attention since limit cycle oscillators are a natural abstraction for systems where periodicity is a distinctive property. Examples of such systems include circadian rhythms and alternate-current power generators.
This work deals with synchronization of pulse-coupled limit cycle oscillators (PCOs). A reverse engineering approach is taken with the objective of obtaining an abstraction for PCO networks able to capture the key properties observed in the classical biological PCO model, to finally implement it in an engineering system. Using our new model, we analyze the existence and stability of synchronization in a variety of PCO network topologies, starting from the simplest all-to-all network where global synchronization is proven to exist, to end giving synchronization conditions for the general strongly connected network case.
Inspired by the strong synchronization properties of PCO networks we design a PCO-inspired time synchronization protocol for wireless sensor networks that enjoys all the advantages of our optimized PCO setup. A pilot implementation is presented going from a simulation stage to a hardware implementation.
UC Santa Barbara Professors Kaustav Banerjee (ECE) & Samir Mitragotri (ChemE) and graduate student researcher Deblina Sarkar demonstrate atomically thin, ultrasensitive and scalable molybdenum disulfide field-effect transistor based biosensors and establish their potential for single-molecule detection
Move over, graphene. An atomically thin, two-dimensional, ultrasensitive semiconductor material for biosensing developed by researchers at UC Santa Barbara promises to push the boundaries of biosensing technology in many fields, from health care to environmental protection to forensic industries.
Based on molybdenum disulfide or molybdenite (MoS2), the biosensor material — used commonly as a dry lubricant — surpasses graphene’s already high sensitivity, offers better scalability and lends itself to high-volume manufacturing. Results of the researchers’ study have been published in ACS Nano.
“This invention has established the foundation for a new generation of ultrasensitive and low-cost biosensors that can eventually allow single-molecule detection — the holy grail of diagnostics and bioengineering research,” said Samir Mitragotri, co-author and professor of chemical engineering and director of the Center for Bioengineering at UCSB. “Detection and diagnostics are a key area of bioengineering research at UCSB and this study represents an excellent example of UCSB’s multifaceted competencies in this exciting field.”
The key, according to UCSB professor of electrical and computer engineering Kaustav Banerjee, who led this research, is MoS2’s band gap, the characteristic of a material that determines its electrical conductivity.
UC Santa Barbara jumps seven spots to No. 15 in Washington Monthly magazine’s Sept/Oct issue list of the best national universities
- UCSB is also ranked number 13 in Washington Monthly’s “Best Bang for the Buck” rankings
- Among public universities, UCSB is #11 among National Universities and #12 in the Best Bang for the Buck rankings
The University of California dominated Washington Monthly’s 2014 list, with UC San Diego taking the top spot, and UC Riverside and UC Berkeley ranking second and third, respectively. UCLA is ranked #5, UCSB #15 and UC Davis #16. “All eight of the UC campuses that were ranked in the top 100 institutions deserve heartfelt congratulations from the entire UC community,” said UC President Janet Napolitano.
While U.S. News & World Report usually awards its highest ratings to private universities, the editors of Washington Monthly prefer to give public universities more credit, and higher rankings. Fifteen of the top 20 universities in the Washington Monthly rankings are taxpayer-funded.
Emerging engineered systems dwarf their predecessors in scale. As a result, minimalistic design approaches that extract the essential features of the problem at hand have become compelling. Adopting such designs from the very outset enables us to decrease the problem scale to manageable levels. In this talk, we consider two examples which illustrate the benefits of this approach.
We first consider the problem of estimating continuous valued parameters from a few random projections of a high dimensional signal (compressive measurements). A direct application of standard compressed sensing based on discretization suffers from performance loss due to basis mismatch. We show that this is not an inherent limitation of compressive measurements. To this end, we consider lower bounds on estimation error variance, the Cramer Rao Bound (CRB) and the Ziv-Zakai Bound (ZZB) and show that random projections preserve these bounds up to an SNR penalty equal to the dimensionality reduction factor. We show how the convergence of the ZZB to the CRB can be used to tightly predict the number of compressive measurements needed to avoid gross estimation errors. We illustrate these ideas using the example of channel estimation for large 60GHz arrays.
The second problem we consider is that of identifying a user’s interests from just his/her tweet times. By using the known timing of “events” associated with a topic (such as the times when a baseball team plays its games), we are able to identify users interested in this topic (the baseball team). We also show how the timing of these events can themselves be inferred from aggregate Twitter feeds obtained by querying Twitter with a few keywords related to the topic.
The 2014 Academic Ranking of World Universities (ARWU) places UC Santa Barbara Engineering/Technology and Computer Science as #7 in the world
UCSB also received a perfect score of 100 for engineering in the criteria category of percentage of papers published in the top 20% of journals in engineering fields. According to ARWU’s report on methodology, the top 20% journals are defined as “their impact factors in the top 20% of each ISI category according to Journal Citation Report, 2012” and that the score is calculated as “the number of papers in the top 20% journals of a particular broad subject field to that in all journals of the field.”
ARWU uses six objective indicators to rank world universities, including the number of alumni and staff winning Nobel Prizes and Fields Medals, number of highly cited researchers selected by Thomson Reuters, number of articles published in journals of Nature and Science, number of articles indexed in Science Citation Index – Expanded and Social Sciences Citation Index, and per capita performance of a university.