Abstract: Bitcoin is a very successful digital currency. Important question that Bitcoin miners need to consider is whether the investment in a new piece of hardware will pay off, versus simply buying the BTC on an exchange. It is embarrassing to buy a bitcoin mining ring and never recoup the original BTC cost in the profits, especially since maintaining the rigs requires round the clock monitoring and considerable energy bills. A simple solution is to evaluate the return of the mining operation in terms of BTC. Here we look at how the Goldstrike 1 miner works to be energy efficient. We also take a look at various cooling techniques that would help reduce heat radiation and in turn reduce energy consumption. A strategic placement of heat detector systems is also needed for large mining farms, that will be discussed as well.

1. 1
Abstract: Bitcoin is a very successful digital currency. Important question that Bitcoin miners need to consider is whether the
investment in a new piece of hardware will pay off, versus simply buying the BTC on an exchange. It is embarrassing to buy a
bitcoin mining ring and never recoup the original BTC cost in the profits, especially since maintaining the rigs requires rou nd the
clock monitoring and considerable energy bills. A simple solution is to evaluate the return of the mining operation in terms of
BTC. Here we look at how the Goldstrike 1 miner works to be energy efficient. We also take a look at various cooling techniqu es
that would help reduce heat radiation and in turn reduce energy consumption. A strategic placement of heat detector systems is
also needed for large mining farms, that will be discussed as well.
Index Terms—Bitcoin, Energy Consumption, Cooling techniques
I. INTRODUCTION1
With paper money, a government decides when to print
and distribute money. Bitcoin doesn't have a central
government. With Bitcoin, miners use special software to
solve math problems and are issued a certain number of
bitcoins in exchange. This provides a smart way to issue the
currency and also creates an incentive for more people to
mine. On Dec. 16, 2014, when PandoDaily published its
article, they recorded a global hashrate of 7,000,000
Gigahash per second (Gh/s). That’s a lot of hash.
Blockchain.info, when they were still listing the statistics,
estimated the electricity consumption this required by using
a rate of 650 watts per Gh/s. So, with a little multiplication we
find that means the Bitcoin network was supposedly
drawing 9.55 gigawatts.
Multiply that by 24 to discover that Bitcoin was
purportedly using 229.2 gigawatt-hours per day.
But the actual Blockchain.info stat for energy
consumption (which PandoDaily quoted) was 301 gigawatt-
hours per day. Think about that for a month and then
1
Paper was submitted for review on 6th
November, 2015.
Author: Kanishk Agarwal (I001), MPSTME, NMIMS University
3rd
year.
Author: Atharv Johri (I014), MPSTME, NMIMS University 3rd
year.
Author: Idhanta Kakkar (I015), MPSTME, NMIMS University 3rd
year.
imagine paying the bill for electricity for just 1 year of
mining.
Two factors dictate energy costs. Energy cost and the
energy consumption of the part. Advantage will be for those
that have cheaper access to energy and already have their
cost of mining hardware paid off when returns on hashing
were higher. Cheaper energy allows themto pay to their
newly acquired hardware over longer cycles and to continue
operation even when $ per Gh/s drops very low. An
advantage for others may be because they have more
energy efficient designs.
In the case of Bitcoin, the profitability is a direct function of
the silicon, with few other factors except access to electricity
and cooling.
II. TECHNIQUES
A. Energy Factor
An important question that Bitcoin miners need to consider
is whether the investment of USD in a new piece of
hardware will pay off, versus simply buying the BTC on an
exchange. Many customBTC mining rigs (or shares in
companies that maintain themon your behalf) are
denominated in BTC4, so it's embarrassing to buy such a rig
and never recoup the original BTC cost in its mining profits,
especially since maintaining the rigs requires round-the-
clock monitoring and considerable energy bills. A simple
Improving Energy Efficiency Of Bitcoin Mining Processor
NMIMS Mukesh Patel School of Technology Management and Engineering
Kanishk Agarwal – kanishkagarwal@hotmail.com
Atharv Johri – johriatharv@gmail.com
Idhanta Kakkar – idhanta@gmail.com

2. solution is to evaluate the return of the mining operation in
terms of BTC.
Of course, there are two factors that dictate energy costs -
the cost of energy, and the energy consumption of the part.
The parties with the greatest advantage will be those that
have cheaper access to large quantities of energy and
already have their mining hardware paid off when returns on
hashing were higher. Cheaper energy allows these parties to
pay to their newly acquired hardware over longer cycles,
and to continue to operate even when $ per Gh/s drops
precipitously low. Others may have an advantage because
they have more energy efficient hardware designs.
B. Goldstrike 1 Miner
The chip consists of 120 hash engines, and at a target clock
frequency of 1.05GHz, delivers 126GH/s. Each hash engine
solves a different problem – so at any given moment there
are 120 different problems being worked on. There is a 128
entry deep and 384-bits wide input FIFO and a 128 entry
deep 65-bits wide output FIFO. A SPI controller writes the
data into the input FIFO and reads the data out of the
output FIFO. Every time a new data is shifted into the input
FIFO, output data from the output FIFO is shifted out. A
PLL is used to synthesize core clock from a 50MHz crystal
input. A thermal diode is used to monitor the die
temperature and adjust supply voltage and clock frequency
to keep the die temperature within safe limits at the system
level. The input interface loads a new work from the input
FIFO into the working registers of the hash engine, when
the previously
loaded work is completed. Each hash engine performs two
rounds of SHA-256 processing. SHA-256 algorithmitself
consists of 64 iterations. Both rounds and iterations are fully
unrolled, resulting in a 128-stages deep pipelined
architecture.
Hence at any given instant of time, there are 128 different
nonce values being processed in the pipeline.
After an initial latency of 128 cycles, a hash output is
produced every clock cycle and is checked by a
comparator logic to test if the “target” criteria is met. If the
target is met, it is handed over to the output
interface to place the result in the output FIFO.
Fabrication & Packaging
This design was fabricated on Global Foundries 28nm
HKMGprocess with 9 metal layers. A die picture
with an 11 x11 grid layout implementation is shown in
Figure 6.a. The center grid boxcontains the
entire top level logic. Each of the remaining 120 grey squares
in the grid represents a single hash engine.
There are routing channels between the hash engine blocks
to route the input and output buses and
other control signals to the cores. All the signal I/O ports
are located on the right edge of the die.
To reduce our overall systemcost and meet power delivery
and thermal dissipation requirements, we
decided to put 4 bare dies in a 37.5 mmx37.5mmFCBGA
package.
Each die can
deliver 126GH/s @ 1.05GHz and 0.7V, consuming
approximately 125W, hence the four die package
delivers the desired 504 GH/s at 500W
Based on this power and performance, this design has an
energy efficiency of 1GH/J.
SystemDesign
Terraminer™ IVappliance consists of a master control
processor (MCP) and two Goldstrike™ boards,
with two Goldstrike™ packages per board. The MCP is
connected to the bitcoin network via its Ethernet
connection and run the bitcoin protocol software.
The MCP communicates with the Goldstrike™ boards via
USB connection. The Goldstrike™ board
converts USB to the SPI and I2C protocols for
communication with the Goldstrike™ ASICs and other
devices such as remote temperature sensing, fan speed and
other control functions.
Design Challenges
Some of the interesting design challenges we faced include
high node toggle rates, high
power density and high sequential to combinational ratio.
The very nature of SHA-256 algorithmcreates randombit
patterns resulting in toggle rates exceeding
80% on data path nodes in the hash engine. Compact
physical layout for reduced die area combined
with high toggle rates produced very high power density for
this design.
A fully unrolled SHA-256 algorithmimplementation resulted
in a design with sequential cell count
exceeding 50% of total. Distributing clock to all the
sequential cells and closing timing was definitely a
big challenge and it took several iterations.
Ensuring that the die junction temperature stayed within the
105oC design specification in a system
dissipating excess of 2.4KW was another challenge.
Efficiency of Goldstrike
Each die can deliver 126GH/s @ 1.05GHz and 0.7V,
consuming approximately 125W, hence the four die package
delivers the desired 504 GH/s at 500W.Based on this power
and performance, this design has an energy efficiency of
1GH/J. This can be bettered to achieve even higher
efficiency.

3. 3
Now that we know how the basic miner works, imagine a
full room with these, they will generate a lot of heat and will
need efficient heat detection systems and proper cooling
techniques. Further lets see how the heat detection system
works and how we can use it to detect which source is
emitting the maximumamount of heat.
C. Strategic Sensor Placement
Studies have shown that a significant portion of the total
energy consumption of many data centers is caused by the
inefficient operation of their cooling systems. Without
effective thermal monitoring with accurate location
information, the cooling systems often use unnecessarily
low temperature set points to overcool the entire room,
resulting in excessive energy consumption.
CRAC – Computer Room Air Conditioning accounts for
up to half of the energy consumption. Different techniques
are needed to cool the place, eg, HP engineers used an
ingenious way of vent-tile technology.
Current solution is simplistic placement, which based off
of assumptions, helps to locate where the sensors should be
placed to immediately detect from where the heat is
generated.
But a better solution would be using mathematics to
actually calculate where the sensors should be placed.
Here we use a technique based on CFD – Computation
Fluid Dynamics. Using CFD we try to solve 2 problems:
1. When the number of sensors is given, we seek to
place all the given sensors in the data center.
2. Considering the still high cost of each wireless
temperature sensor, we seek to minimize the
number of sensors needed.
Hot Server Detection Model
Data Fusion technique is used to improve the detection
performance of sensors.
R - Fusion Radius
Use a simple data fusion scheme to calculate the
average temperature fromall the sensors with the fusion
radius of the monitored spot.
Compare that value with η
If the result is larger than η then detection is positive.
Use normal distribution to figure out noise.
Tm(x; y; z) = Tr(x; y; z) + Ni
2
Tr being real temperature at that location without noise.
Detection Probability Maximization
By using Detection Probability Maximization, we can
pinpoint from where the maximum heat is coming by
increasing the probability of the sensor to detect where the
heat is most and report that instead of reporting where the
heat generated is lower than critical temperature.
M - number of locations in data center roomwhere temp
needs to be monitored.
Given a limited and reasonable number of sensors, N <
M, we need to find the placement of these N sensors
such that we can detect the overheating emergency at
any of the M locations with the highest possible
confidence.
PF - False alarm rate of reporting an overheating
emergency
PD - the detection probability of an overheating
emergency
N - Sensor number within the fusion region
1/M ΣPD
PF < ɑ (detection false alarmrate)
n can be calculated:
n= σXn^-1(1- ɑ)/n + C
C – constant
Sensor Number Minimisation
Another problemin thermal monitoring is minimising the
number of placed sensors.
Cost of each sensor is high plus installation cost of many
together is high too.
To achieve the goal of sensor number minimisation.
Change the section probability objective as a constraint.
Assume M locations in data center room whose
temperatures need to be monitored, we beed to minimise
sensor number N that can oiled a detection probability
higher than β and false alarmrate lower than ɑ
Arg min N
Subject to the constraints:
PF(SN) < ɑ 1<i<M
PD(SN) > β 1<i<M
SN - List of Locations of all the N sensors
We have looked in-depth on how we can do better sensor
placement to detect the heat generated. Now let us look at
the cooling techniques that can be used to minimize and
mitigate the heat so that energy consumption is less and
also, damages don’t occur.

4. III. COOLING TECHNIQUES
Different cooling techniques are needed in order to find the
most suitable one that would fit our requirement, here are
some:
1. Immersion Cooling
It is used to cool high heat fluxcomponents. Unlike the
water-cooled cold plate approaches which utilize physical
walls to separate the coolant fromthe chips, immersion
cooling brings the coolant in direct physical contact with
the chips. As a result, most of the contributors to internal
thermal resistance are eliminated, except for the thermal
conduction resistance fromthe device junctions to the
surface of the chip in contact with the liquid.
Direct liquid immersion cooling offers a high heat transfer
coefficient which reduces the temperature rise of the heated
chip surface above the liquid coolant temperature. The
magnitude of the heat transfer coefficient depends upon the
thermo physical properties of the coolant and the mode of
convective heat transfer employed.
2. Air-Cooled Systems
Forced air-cooled systems may be further subdivided into
serial and parallel flow systems. In a serial flow systemthe
same air streampasses over successive rows of modules or
boards, so that each row is cooled by air that has been
preheated by the previous row. Depending on the power
dissipated and the air flow rate, serial air flow can result in a
substantial air temperature rise across the machine. The rise
in cooling air temperature is directly reflected in increased
circuit operating temperatures. This effect may be reduced
by increasing the air flow rate. Parallel air flow systems have
been used to reduce the temperature rise in the cooling air.
3. Hybrid Air–Water Cooling
An air-to-liquid hybrid cooling systemoffers a method to
manage cooling air temperature in a systemwithout
resorting to a parallel configuration and higher air flow rates.
In a systemof this type, a water-cooled heat exchanger is
placed in the heated air streamto extract heat and reduce the
air temperature.
Thus the hybrid air-water cooling technique is the most
suited method here and can be used.
IV. DYNAMIC CACHE RESOURCE POOLING
One way to improve the energy efficiency of the bitcoin
mining processor is change the structure of the processor. It
should have the dynamic cache pooling in 3d stacked
multicore processor for improving the energy efficiency.
In resource pooling multiple components are shared among
different cores and it also reduces the total chip area.
Since in the 3d stacked processors the cache is stacked
vertically with the help of tsvs helping in efficient pooling of
the resources.
There are three basic things we need to keep in mind.
First, 3d stacked architecture with cache pooling,
Application-aware job allocation and the evaluation of
dynamic cache resource pooling.
According to the proposed structure the l2 caches are
stacked one above theanother using the silicon compound,
TSV.
The objective of this design is to increase the performance
by increasing the cache size whenever need and to save
power by turning off the unused cache partitions.
Also to implement the cache resource pooling we need to
modify cache status registers and cache control logic.
LCSR is used to record the status of the cache partitions
and Remote Cache Status Registers to keep l1 cache aware
of the processes.
According to the runtime application-aware job allocation
and cache pooling policy the cache hungry jobs should be
stacked with less cache hungry jobs so as to efficiently
share the pool of cache.
This policy contains two stages:
1. Job Allocation and 2.Cache resource pooling.
JobAllocation
First the jobs are allocated to caches randomly and within
few seconds using a pre defined regression based predictor
the jobs are sorted on the basis of their performance
improvements .
Among four cache Jobs(j1,j2,j3,j4)where j1 the most cache
hungry and j4 least cache hungry, so j1 and j4 are grouped
together and j2 and j3 are grouped together.. Also j2 and j3
are placed near the heat sink so they can give away heat fast
making it more efficient.
Cache Resource Pooling
It is a method to manage job pair. A threshold (t) is used to
determine whether a job nees more cache partition. This
threshold represent the minimumimprovement that resuls in
alower EDP.
Also the results states that the EDP and EDAP went up by
39.2% and 57.2% in comparison with the#d systems with
static cache sizes.
V. CONCLUSION
As the difficulty level for Bitcoin mining continues to
increase, there is a continued effort to build new Bitcoin
mining processor with reduced power consumption and
increased performance. Apart from migrating the design to
the latest process technologies, there are still some
opportunities to improve performance and reduce power
through customizing the micro architecture and circuit
design. Goldstrike™ 1 processor has energy efficiency of
1GH/J and that this can be increased to greater than 4GH/J.
Ultimately, for innovation in the hardware space, we need

5. 5
lots of new ideas to be tried out for cheap. However, the
semiconductor model has increasingly moved away from
this direction to expensive chips. As a result, chip startups
are largely non-existant and there are few markets in which
high- risk, innovative ideas can be examined. At the same
time, demand for hardware engineers is dropping and fresh
hard-ware talent is being diverted away from hardware
companies to software companies that offer higher salaries.
This creates an increasingly unhealthy death spiral where
fewer new ideas are being tried and the top talent is leaving
the field. We need to think about strategic ways to enable
cheaper chips for new ideas, through open-source CAD
tools, for instance, or new technologies to reduce chip
costs, or more fluid financing methods that spread risk
better, and through better education and training, in order to
enter the Age of Bespoke Silicon.
Future Scope
The techniques mentioned here in the paper can be
applied to data mining farms to improve the energy
efficiency of the processors. The Processor’s architecture
and cache can further be modified to solve the heating
problem to reduce the energy footprint. Research can be
done on cooling liquids which can be used instead of
dielectrics in immersion cooling to provide a better heat sink.
REFERENCES
1. GOLDSTRIKE™1: COINTERRA’S FIRST GENERATION CRYPTO-
CURRENCY MINING PROCESSOR FOR BITCOIN
JAVED BARKATULLAH, TIMO HANKE
2. BITCOIN MINING AND ITS ENERGY FOOTPRINT
KARL J. O'DWYERT AND DAVID MALONE* HAMILTON
INSTITUTE NATIONAL UNIVERSITY OF IRELAND
MAYNOOTH
3. BITCOIN AND THE AGE OF BESPOKE SILICON
MICHAEL BEDFORD TAYLOR UNIVERSITY OF CALIFORNIA,
SAN DIEGO
4. DYNAMIC CACHE POOLING FOR IMPROVING ENERGY
EFFICIENCY IN 3D STACKED MULTICORE PROCESSORS
JIE MENG, TIANSHENG ZHANG, AND AYSE K. COSKUN
ELECTRICAL AND COMPUTER ENGINEERING DEPARTMENT,
BOSTON UNIVERSITY, BOSTON, MA, USA
5. INTELLIGENT SENSOR PLACEMENT FOR HOT SERVER
DETECTION IN DATA CENTERS
XIAODONG WANG, XIAORUI WANG, MEMBER, IEEE,
GUOLIANG XING, MEMBER, IEEE, JINZHU CHEN, CHENG-
XIAN LIN, AND YIXIN CHEN, SENIOR MEMBER, IEEE