salfter/infnoise
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

updates

Browse Source
This commit is contained in:
Bill Cox
2014-10-10 14:25:12 -04:00
parent 0881c014ff
commit 6524d27441
1 changed files with 12 additions and 12 deletions
Download Patch File Download Diff File Expand all files Collapse all files

24
README.md
View File

@@ -1,5 +1,17 @@
##Infinite Noise Multiplier
The Infinite Noise Multiplier (INM) is an architecture for true random number generators (TRNG).
Besides being simple, low-cost, and fast, it is easy to get right, unlike other TRNGs.
INMs are suitable for both board level implementation, and ASIC implementation. Speed is
limited by the speed of a voltage buffer and comparator, and can run in excess of 100
Mbit/second per second with high performance components. Cheap solutions with CMOS quad
op-amps can run at 6Mbit/second.
Adjacent bits from an INM are correlated, so whitening is required before use in
cryptography. However, the output has a highly predictable amount of entropy for easy
estimation of bits added to an entropy pool.
### An Eagle open-source board is under way
Here's the schematic so far...
@@ -15,18 +27,6 @@ Noise Multiplier")
![Schematic of Infinite Noise Multiplier](infnoise_small/schematic.png?raw=true "Infinite
Noise Multiplier")
The Infinite Noise Multiplier (INM) is an architecture for true random number generators (TRNG).
Besides being simple, low-cost, and fast, it is easy to get right, unlike other TRNGs.
INMs are suitable for both board level implementation, and ASIC implementation. Speed is
limited by the speed of a voltage buffer and comparator, and can run in excess of 100
Mbit/second per second with high performance components. Cheap solutions with CMOS quad
op-amps can run at 6Mbit/second.
Adjacent bits from an INM are correlated, so whitening is required before use in
cryptography. However, the output has a highly predictable amount of entropy for easy
estimation of bits added to an entropy pool.
### The Problem: Noise Sensitivity, and Signal Injection
True random number generators are very difficult to get right. Generally, they amplify a