BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
X-LIC-LOCATION:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20240626T180035Z
LOCATION:3010\, 3rd Floor
DTSTART;TZID=America/Los_Angeles:20240625T163000
DTEND;TZID=America/Los_Angeles:20240625T164500
UID:dac_DAC 2024_sess127_RESEARCH970@linklings.com
SUMMARY:Efficient Approximate Decomposition Solver using Ising Model
DESCRIPTION:Research Manuscript\n\nWeihua Xiao (Shanghai Jiao Tong Univers
 ity), Tingting Zhang (University of Alberta), Xingyue Qian (Shanghai Jiao 
 Tong University), Jie Han (University of Alberta), and Weikang Qian (Shang
 hai Jiao Tong University)\n\nComputing with memory is an energy-efficient 
 computing approach. It pre-computes a function and store its values in a l
 ookup table (LUT), which can be retrieved at runtime. Approximate Boolean 
 decomposition has been recently proposed to reduce the LUT size for implem
 enting complex functions, but it takes a long time to find a decomposition
  with a minimized error. As a parallel algorithm developed based on the Is
 ing model, simulated bifurcation (SB) is promised to be a high-performance
  approach for combinatorial optimization. In this paper, we propose an eff
 icient SB-based approximate function decomposition approach. Specifically,
  a new approximate disjoint decomposition method, called column-based appr
 oximate disjoint decomposition, is first proposed to fit the Ising model. 
 Then, it is adapted to the Ising model-based optimization solver. Moreover
 , two improvement techniques are developed for an efficient search of the 
 approximate disjoint decomposition when using SB. The experiment results s
 hows that compared to the state-of-the-art work, our approach achieves a 1
 1% smaller mean error distance with an average 1.16× speedup when approxim
 ately decomposing 16-input 16-output Boolean functions.\n\nTopic: Design\n
 \nKeyword: Emerging Models of Computation\n\nSession Chairs: Sudhakar Pama
 rti (UCLA) and Qingxue Zhang (Purdue University)
END:VEVENT
END:VCALENDAR