BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Penn Engineering Events - ECPv6.15.18//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Penn Engineering Events
X-ORIGINAL-URL:https://seasevents.nmsdev7.com
X-WR-CALDESC:Events for Penn Engineering Events
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20230312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20231105T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230731T130000
DTEND;TZID=America/New_York:20230731T140000
DTSTAMP:20260404T050024
CREATED:20230616T132724Z
LAST-MODIFIED:20230616T132724Z
UID:10007590-1690808400-1690812000@seasevents.nmsdev7.com
SUMMARY:PSOC@Penn Seminar:  Yiming Wang & Ison Chen
DESCRIPTION:
URL:https://seasevents.nmsdev7.com/event/psocpenn-seminar-kshitiz-parihar-ison-chen/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar,Doctoral,Graduate,Student
ORGANIZER;CN="PSOC":MAILTO:manu@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230801T100000
DTEND;TZID=America/New_York:20230801T113000
DTSTAMP:20260404T050024
CREATED:20230720T194435Z
LAST-MODIFIED:20230720T194435Z
UID:10007610-1690884000-1690889400@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Mechanics- Informed Optimization for Enhanced Adhesion and Toughness"
DESCRIPTION:Structural design optimization has long played a crucial role in engineering\, often with the goal of creating stiff and lightweight structures for aerospace and other applications. However\, optimizing structures against failure is also crucial and has been much less well explored. Failure via fracture and at interfaces is particularly challenging in design optimization as they involve high local stress concentrations and singular stresses. In this talk\, we demonstrate how mechanics models can be integrated with optimization schemes to engineer structures with improved interface adhesion and fracture toughness. Specifically\, four distinct structural design cases are considered: adhesive fibrils\, shear lap joints\, architected adhesive joints\, and adaptive mechanical networks. The computational framework couples finite element analysis with multiple optimization methods\, including gradient and heuristic-based techniques\, as well as machine learning-based approaches. We show that performance can be improved by formulating optimization schemes and objective functions based on the principles of mechanics and failure. Optimal designs are determined via the computational schemes and validated via experiments on several different material systems. The versatility of the computational and optimization schemes that have been developed enables them to be extended to other scenarios where performance can be improved by optimizing geometry to control stresses.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-mechanics-informed-optimization-for-enhanced-adhesion-and-toughness/
LOCATION:Towne 337
CATEGORIES:Seminar,Doctoral
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230803T150000
DTEND;TZID=America/New_York:20230803T170000
DTSTAMP:20260404T050024
CREATED:20230731T202358Z
LAST-MODIFIED:20230731T202358Z
UID:10007620-1691074800-1691082000@seasevents.nmsdev7.com
SUMMARY:ESE PhD Thesis Defense: "Software/Hardware Co-optimization for Computer Systems with 3D-stacking Memories"
DESCRIPTION:Emerging 3D memory technologies\, such as the Hybrid Memory Cube (HMC) and High Bandwidth Memory (HBM)\, provide high bandwidth and massive memory-level parallelism. With the growing heterogeneity and complexity of computer systems (CPU cores and accelerators\, etc.)\, efficiently integrating emerging memories into existing systems poses new challenges to both algorithm\, hardware and system. This dissertation explores the application-aware system optimization techniques for 3D-stacking memory in both domain-specific accelerators (DSAs) and general-purpose computer systems. The first part of the dissertation presents a standalone 3D-Stacking memory-based graph accelerator that can achieve 45.8 billion traversed edges per second (TEPS) by co-optimizing the algorithm and the hardware architecture. We first present the modifications of algorithm and a platform-aware graph processing architecture to perform level-synchronized breadth first search (BFS) on FPGA-HMC platform. To gain better insights into the potential bottlenecks of proposed implementation\, we develop an analytical performance model to quantitatively evaluate the HMC access latency and corresponding BFS performance. Based on the analysis\, we propose a two-level bitmap scheme to reduce memory access and perform optimization on key design parameters (e.g. memory access granularity). Then\, we leverage the inherent graph property i.e. vertex degree to co-optimize algorithm and hardware architecture. In particular\, we first develop two algorithm optimization techniques: degree-aware adjacency list reordering and degree-aware vertex index sorting and two platform-dependent hardware optimization techniques\, namely degree-aware data placement and degree-aware adjacency list compression. These two techniques together substantially reduce the amount of access to external memory. Finally\, we conduct extensive experiments on an FPGA-HMC platform to verify the effectiveness of the proposed techniques. In the second part of this dissertation\, we develop machine learning methods that can automatically identify access patterns of major variables in a program. These methods can then cluster these variables with similar access patterns to reduce the overhead for SDAM. Our evaluation on standard CPU benchmarks and data-intensive benchmarks (for both CPU and accelerators) demonstrates a 1.41x and1.84x speedup on CPU and a 2.58x speedup on near-memory accelerators in our system with SDAM\, compared to a baseline system.
URL:https://seasevents.nmsdev7.com/event/ese-phd-thesis-defense-software-hardware-co-optimization-for-computer-systems-with-3d-stacking-memories/
LOCATION:Moore 317\, 200 S 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Dissertation or Thesis Defense
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
END:VCALENDAR