Probabilistic Computing

As technology scales below 100nm and operating frequencies increase, correct operation of nano-CMOS will be compromised due reduced device-to-device distance, imperfections, and low noise and voltage margins. Unlike traditional faults and defects, these errors are expected to be transient in nature. Unlike radiation related upset errors, the propensity of these transient errors will be higher. Due to these highly likely errors, it is more appropriate to model nano-domain computing as probabilistic rather than deterministic events. We propose the formalism of probabilistic Bayesian networks (BNs), which also forms a complete joint probability model, for probabilistic computing. Using the exact probabilistic inference scheme known as clustering, we show that for a circuit with about 250 gates the output error estimation time is less than three seconds on a 2GHz processor. This is three orders of magnitude faster than a recently proposed method for probabilistic computing using transfer matrices.

Source: IEEE Xplore

今日落街下午茶，行過7-11望下今期PC-Market
覺得幾得意就係隔離間報攤買左本黎睇
其中一個topic係講Probabilistic Computing
覺得幾有趣就搵左篇外國文章黎睇
幾得意！

佢講到即使運算有error，但亦係「Transient」
用係Lossy Compression上，呢D運算錯誤係難以察覺
假如你花心機係Photo-hunt上
咁就不如將D圖保存為Tga、Tif或RAW 吧啦
一堆屎上面加舊榴槤，唔會有咩大分別！

不過都有人唔同意呢種運算方式
就好似有人覺得榴槤勁好味同一堆屎好唔同咁
我自己就幾鐘意，嘗試去除「運算一定要準確」既舊規則
呢個新衝擊話唔定會係寶黎！