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Shortest Verilog Code

posted Jan 21, 2019, 10:40 PM by MUHAMMAD MUN`IM AHMAD ZABIDI   [ updated Jan 21, 2019, 10:46 PM ]

Guess what it does?

module smart( input [2:0] x, output [7:0] y);
  assign y = 1 << x;
endmodule

It's a 3:8 decoder!

The following is how it's done, normally:

module naive(sel, res);
  input [2:0] sel;
  output [7:0] res;
  reg [7:0] res;

  always @(sel or res)
    begin
    case (sel)
      3'b000 : res = 8'b00000001;
      3'b001 : res = 8'b00000010;
      3'b010 : res = 8'b00000100;
      3'b011 : res = 8'b00001000;
      3'b100 : res = 8'b00010000;
      3'b101 : res = 8'b00100000;
      3'b110 : res = 8'b01000000;
      default : res = 8'b10000000;
    endcase
  end
endmodule

Now why would you want to do normal?

SKEE2263 1891 Milestone 6 Submissions

posted Jan 6, 2019, 5:40 AM by MUHAMMAD MUN`IM AHMAD ZABIDI   [ updated Jan 12, 2019, 12:04 AM ]

Group 1:

YouTube Video



Group 2:

YouTube Video



SKEE2263 1891 Milestone 5 Submissions

posted Jan 6, 2019, 5:36 AM by MUHAMMAD MUN`IM AHMAD ZABIDI   [ updated Jan 10, 2019, 2:00 AM ]

Task: Datapath for Naive Multiplier
Submitted by Groups 1, 2, 4 & 5.

Group 1 - Jamil, Hazin, Azrin & Fatin:

YouTube Video



Group 2 - Hafizuddin, Syahir & Zulazwan:

YouTube Video



Group 4 - Md Shukri & Wan Naim

YouTube Video



Group 5 - Wan Ahmad:

YouTube Video



SKEE2263 1891 Milestone 4 Submissions

posted Jan 6, 2019, 5:28 AM by MUHAMMAD MUN`IM AHMAD ZABIDI   [ updated Jan 9, 2019, 10:03 PM ]

Task: Sequence Detector with Finite State Machine
Submitted by Groups 1-5.

Group 1 - Jamil, Hazin, Azrin & Fatin:

YouTube Video



Group 2 - Hafizuddin, Syahir & Zulazwan:

YouTube Video



Group 3 - Gautham, Rais & Zulhilmi

YouTube Video



Group 4 - Syukri & Wan Mohd Naim

YouTube Video



Group 5 - Wan Muhammad Fadzli:

YouTube Video



SKEE2263 1891 Milestone 2 Submissions

posted Jan 6, 2019, 5:16 AM by MUHAMMAD MUN`IM AHMAD ZABIDI   [ updated Jan 6, 2019, 5:08 PM ]

Task: Basic I/O with EPM240 CPLD Board

Group 1 - Jamil, Hazin, Azrin & Fatin:

YouTube Video



Group 2 - Hafizuddin, Syahir & Zulazwan:

YouTube Video



Group 3 - Zulhilmi, Gautham & Rais:

YouTube Video



Group 4:

YouTube Video



Group 5 - Wan Muhammad Fadzli:

YouTube Video



Group 6 - Fara Hanis, Khairul Izwan & Nik Mohd Hazlin:

YouTube Video



SKEE2263 1891 Milestone 3 Submissions

posted Jan 5, 2019, 10:28 PM by MUHAMMAD MUN`IM AHMAD ZABIDI   [ updated Jan 6, 2019, 5:02 PM ]

Task: Accumulator based counter with reset and 7-seg display.

Group 1:

YouTube Video



Group 2:

YouTube Video



Group 3:

YouTube Video



Group 4:

YouTube Video



Group 5:

YouTube Video



Group 6:

YouTube Video

Language Models

posted Dec 5, 2018, 2:42 AM by MUHAMMAD MUN`IM AHMAD ZABIDI   [ updated Dec 5, 2018, 2:51 AM ]

Language modeling is key to many interesting problems such as speech recognition, machine translation, or image captioning.  The goal of the problem is to fit a probabilistic model which assigns probabilities to sentences. It does so by predicting next words in a text given a history of previous words.

Language modeling is key to many interestin

g problems such as speech recognition, machine translation, or image captioning.

clinfo

posted Dec 3, 2018, 10:22 PM by MUHAMMAD MUN`IM AHMAD ZABIDI   [ updated Dec 3, 2018, 10:28 PM ]

clinfo is a simple command-line application that enumerates all possible (known) properties of the OpenCL platform and devices available on the system.
Let's install and see what  I have.

$ cat /proc/cpuinfo  | grep 'name'| uniq
model name : AMD FX(tm)-8350 Eight-Core Processor

$ sudo apt install clinfo

$ clinfo Number of platforms 1 Platform Name NVIDIA CUDA Platform Vendor NVIDIA Corporation Platform Version OpenCL 1.2 CUDA 9.1.84 Platform Profile FULL_PROFILE Platform Extensions cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_fp64 cl_khr_byte_addressable_store cl_khr_icd cl_khr_gl_sharing cl_nv_compiler_options cl_nv_device_attribute_query cl_nv_pragma_unroll cl_nv_copy_opts cl_nv_create_buffer Platform Extensions function suffix NV Platform Name NVIDIA CUDA Number of devices 1 Device Name GeForce GTX 1080 Ti Device Vendor NVIDIA Corporation Device Vendor ID 0x10de Device Version OpenCL 1.2 CUDA Driver Version 390.87 Device OpenCL C Version OpenCL C 1.2 Device Type GPU Device Topology (NV) PCI-E, 01:00.0 Device Profile FULL_PROFILE Device Available Yes Compiler Available Yes Linker Available Yes Max compute units 28 Max clock frequency 1582MHz Compute Capability (NV) 6.1 Device Partition (core) Max number of sub-devices 1 Supported partition types None Max work item dimensions 3 Max work item sizes 1024x1024x64 Max work group size 1024 Preferred work group size multiple 32 Warp size (NV) 32

10 Best Links: Word Embeddings

posted Nov 2, 2018, 8:32 AM by MUHAMMAD MUN`IM AHMAD ZABIDI   [ updated Dec 3, 2018, 8:55 PM ]

A popular idea in modern machine learning is to represent words by vectors. These vectors capture hidden information about a language, like word analogies or semantic.
  1. An introduction to word embeddings
  2. Introduction to Word Embeddings: Problems and Theory
  3. [Hamilton 2016] Hamilton, William L., et al. “Inducing domain-specific sentiment lexicons from unlabeled corpora.” arXiv preprint arXiv:1606.02820 (2016).
  4. [Kusner 2015] Kusner, Matt, et al. “From word embeddings to document distances.” International Conference on Machine Learning. 2015.
  5. [Mikolov 2013a] Mikolov, Tomas, Wen-tau Yih, and Geoffrey Zweig. “Linguistic regularities in continuous space word representations.” hlt-Naacl. Vol. 13. 2013.
  6. [Mikolov 2013b] Mikolov, Tomas, et al. “Efficient estimation of word representations in vector space.” arXiv preprint arXiv:1301.3781 (2013).
  7. [Mikolov 2013c] Mikolov, Tomas, et al. “Distributed representations of words and phrases and their compositionality.” Advances in neural information processing systems. 2013.
  8. [Mikolov 2013d] Mikolov, Tomas, Quoc V. Le, and Ilya Sutskever. “Exploiting similarities among languages for machine translation.” arXiv preprint arXiv:1309.4168 (2013).

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