Merge pull request #3 from czgdp1807/blog-bugs

Removed blog bugs

Author: czgdp1807

_posts/2019-05-30-CPU-GPU-and-TPU-in-Deep-Learning.md

@@ -9,7 +9,7 @@ image: "/images/benchmark-cpu-gpu.png"
 
 GPU (Graphical Processing Unit) is one of the most widely used processing unit used in training data models. It is a single processor chip which frees the CPU cycles from jobs of image processing and mathematical computations. For example, a game with a lot of graphics: many shadows to cast, lots of atmospheric effects, various lighting sources, complex textures, etc. All these eat up extra GPU processing. Also, settings at higher resolutions means each single frame needs more such calculations to just display each pixel on that frame. Everything is handled by the GPU. When the GPU cannot remain at par with the CPU, it causes bottlenecks.
 
-Some stark differences between CPU and GPU
+#### Some stark differences between CPU and GPU
 
 CPU processes data sequentially whereas a GPU has several threads running simultaneously. A GPU thus utilizes parallel computing to increase the speed of training models.
 
@@ -23,18 +23,11 @@ Due to large datasets, the CPU takes up a lot of memory while training the model
 
 The High bandwidth, hiding the latency under thread parallelism and easily programmable registers makes GPU a lot faster than a CPU.
 
-The new Intel Xeon phi processing chip
+#### The new Intel Xeon phi processing chip
 
-It fetches and decodes instructions from four hardware thread execution contexts and has 4 clock latency, hidden by round-robin scheduling of threads
+It fetches and decodes instructions from four hardware thread execution contexts and has 4 clock latency, hidden by round-robin scheduling of threads. Each microprocessor core is a fully functional, in-order core capable of running IA instructions independently of the other cores. It has two pipelines and ring interconnection.
 
-Each microprocessor core is a fully functional, in-order core capable of running IA instructions independently of the other cores
-
-It has two pipelines and ring interconnection.
-
-A comparison between the newly released Xeon phi processor and NVIDIA GeForce GTX 1080
-
-
-The TPU chip
+#### The TPU chip
 
 The chip has been specifically designed for Google's TensorFlow framework. In Google Photos, an individual TPU can process over 100 million photos a day. Google's Cloud TPU is currently only in beta, offering limited quantities and usage. In machine learning training, the Cloud TPU is more powerful in performance and four times larger in memory capacity than Nvidia's best GPU Tesla V100.